I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
14
, N
o.
5
,
O
c
to
be
r
2025
, pp.
3588
~
3598
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
14
.i
5
.pp
3588
-
3598
3588
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
c
or
e
.c
om
Fac
e
S
yn
t
h
:
t
e
xt
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to
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f
ac
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n
e
r
a
t
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on
u
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i
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L
IP an
d
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at
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s
P
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iy
ad
h
ar
s
in
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R
avi
s
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k
ar
1
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h
r
u
t
h
i
D
h
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van
t
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2
, V
ai
s
h
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ave
Je
n
an
e
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h
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2
1
D
e
pa
r
t
m
e
nt
of
A
r
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c
i
a
l
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nt
e
l
l
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ge
nc
e
a
nd D
a
t
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S
c
i
e
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e
, R
a
j
a
l
a
ks
hm
i
E
ngi
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e
r
i
ng C
ol
l
e
ge
, C
he
nna
i
, I
ndi
a
2
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e
pa
r
t
m
e
nt
of
C
om
put
e
r
S
c
i
e
nc
e
, S
r
i
S
i
va
s
ubr
a
m
a
ni
ya
N
a
da
r
C
ol
l
e
ge
of
E
ngi
ne
e
r
i
ng, C
he
nna
i
, I
ndi
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
J
ul
30, 2024
R
e
vi
s
e
d
J
un 27, 2025
A
c
c
e
pt
e
d
J
ul
13, 2025
In
recent
years,
there
have
been
massive
developments
in
the
fi
eld
of
generative
AI,
especially
in
generative
adversarial
networks
(GANs).
GANs
generate
original
images
that
haven'
t
been
seen
during
training
and
have
had
several adva
ncements like StyleGA
N, StyleGAN2,
and StyleGAN2
-
a
daptive
discriminator augmentation
(
ADA
)
. Contrastive
language
-
image pre
-
tr
aining
(CLIP),
by
OpenAI,
is
a
visual
linguis
tic
model
that
has
been
trained
to
associate
texts
with
images.
Recently
,
new
CLIP
variants
were
deve
loped,
such
as
metadata
-
curated
language
-
image
pre
-
training
(MetaCLIP)
,
re
leased
by
Facebook
and
trained
on
a
larger
dataset,
and
Multilinigual
-
CLIP,
which
adapts
CLIP
to
multiple
langu
ages.
We
compare
CLIP
and
its
vari
ants
in
text
-
to
-
face
synthesis
with
a
custom
StyleGAN2
-
ADA
model
and
a
pre
-
trained
StyleGAN2
model.
Our
training
-
free
algorithm
starts
with
an
initial
image
latent
code
that
is
iteratively
manipulated
to
match
a
give
n
text
description.
It
achieves
this
by
minimizing
the
distance
between
the
te
xt
and
image
embedding
in
the
multi
-
modal
embedding
space
of
the
CLIP
models.
An
examination
of
CLIP
and
its
variants
showed
that
Met
aCLIP
outperformed
its
competitors
in
LPIPS
similarity
and
closeness
of
the
synthesized
image
to
the
actual
prompt.
CLIP
produced
the
most
r
ealistic
images
with
the
best
FID
score
and
multilingual
-
CLIP
presented
a
choice
of
input text lang
uage and
genera
ted dece
nt images.
K
e
y
w
o
r
d
s
:
C
L
I
P
G
e
ne
r
a
ti
ve
a
dve
r
s
a
r
ia
l
ne
twor
k
M
e
ta
C
L
I
P
M
ul
ti
li
ngua
l
-
C
L
I
P
S
ty
le
G
A
N
T
e
xt
-
to
-
f
a
c
e
ge
ne
r
a
ti
on
T
e
xt
-
to
-
im
a
ge
ge
ne
r
a
ti
on
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
P
r
iy
a
dha
r
s
in
i
R
a
vi
s
a
nka
r
D
e
pa
r
tm
e
nt
of
A
r
ti
f
ic
ia
l
I
nt
e
ll
ig
e
nc
e
a
nd D
a
ta
S
c
ie
nc
e
, R
a
j
a
la
k
s
hm
i
E
ngi
ne
e
r
in
g
C
he
nna
i,
I
ndi
a
E
m
a
il
:
pr
iy
a
dha
r
s
in
i.
r
@
r
a
ja
la
ks
hm
i.
e
du.i
n
1.
I
N
T
R
O
D
U
C
T
I
O
N
A
r
ti
s
ts
dr
a
w
pi
c
tu
r
e
s
f
r
om
th
e
i
r
im
a
gi
na
ti
on
a
nd
a
r
e
pr
of
ic
i
e
nt
a
t
de
pi
c
ti
ng
va
r
io
us
e
nt
it
ie
s
li
ke
bi
r
ds
,
a
ni
m
a
ls
,
s
c
e
ne
r
y,
a
nd
hum
a
n
f
a
c
e
s
.
T
o
gi
ve
th
e
ir
dr
a
w
in
gs
a
li
f
e
li
ke
a
ppe
a
r
a
nc
e
,
th
e
y
in
c
or
por
a
te
c
ol
or
,
te
xt
ur
e
,
c
om
pos
it
io
n,
a
nd
e
xpr
e
s
s
io
n
s
.
F
ur
th
e
r
m
or
e
,
w
he
n
a
n
a
r
ti
s
t
is
pr
ovi
de
d
a
te
xt
d
e
s
c
r
ip
ti
on,
th
e
y
c
a
n
dr
a
w
a
s
ke
tc
h
th
a
t
c
a
pt
ur
e
s
a
ll
of
th
e
f
e
a
tu
r
e
s
s
pe
c
if
ie
d
w
hi
le
s
im
ul
ta
ne
ous
ly
m
a
in
ta
in
in
g
th
e
r
e
a
li
s
ti
c
a
s
pe
c
ts
.
U
s
in
g
m
a
c
hi
ne
l
e
a
r
ni
ng mode
ls
, w
e
c
a
n m
im
ic
t
hi
s
uni
que
a
bi
li
ty
of
a
r
ti
s
ts
a
nd a
ut
om
a
te
t
he
c
r
e
a
ti
on
of
a
n
im
a
ge
f
r
om
te
xt
,
th
us
dr
a
m
a
ti
c
a
ll
y
r
e
duc
in
g
m
a
nua
l
la
bor
.
G
e
ne
r
a
ti
ve
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
h
a
s
e
vol
ve
d
to
be
one
of
th
e
m
os
t
not
e
w
or
th
y
a
dva
nc
e
s
in
r
e
c
e
nt
ye
a
r
s
in
c
om
put
e
r
vi
s
io
n.
T
o
c
r
e
a
te
ne
w
da
ta
,
g
e
ne
r
a
ti
ve
A
I
le
a
r
ns
pa
tt
e
r
n
s
a
nd
s
e
qu
e
nc
e
s
f
ound
in
da
ta
s
e
t
s
a
m
pl
e
s
.
T
he
pr
im
a
r
y
goa
l
of
te
xt
-
to
-
im
a
ge
s
ynt
he
s
i
s
,
a
br
a
nc
h
of
G
e
n
-
A
I
,
is
to
c
r
e
a
te
a
n
im
a
ge
f
r
om
a
n
in
put
c
a
pt
io
n.
T
he
a
tt
r
ib
ut
e
s
s
pe
c
if
ie
d
in
th
e
te
xt
gui
de
im
a
ge
ge
ne
r
a
ti
on,
a
nd
th
is
pr
oc
e
s
s
ha
s
s
e
v
e
r
a
l
us
e
s
in
a
r
t,
s
t
or
yt
e
ll
in
g,
e
duc
a
ti
on,
a
nd
m
or
e
.
T
e
xt
-
to
-
f
a
c
e
s
ynt
he
s
is
,
a
s
ubf
ie
ld
in
te
xt
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to
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im
a
ge
s
ynt
he
s
is
,
pr
oduc
e
s
a
f
a
c
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l
im
a
ge
f
r
om
a
de
s
c
r
ip
ti
on
a
nd
r
e
qui
r
e
s
gr
e
a
te
r
a
tt
e
nt
io
n
to
de
ta
il
.
H
um
a
n
f
a
c
e
s
c
ont
a
in
m
a
ny
s
ubt
le
ti
e
s
a
nd
m
is
ta
ke
s
in
ge
ne
r
a
te
d
f
a
c
e
s
a
r
e
e
a
s
il
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
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I
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e
ll
I
S
S
N
:
2252
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8938
F
ac
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Sy
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t
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to
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fa
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ge
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in
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and it
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v
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(
P
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in
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is
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)
3589
de
te
c
ta
bl
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T
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r
e
ha
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b
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m
a
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a
dv
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m
e
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th
r
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r
a
ti
ve
a
dve
r
s
a
r
ia
l
ne
twor
ks
(
G
A
N
s
)
[
1]
a
nd
di
f
f
us
io
n m
ode
ls
[
2]
.
G
A
N
s
[
1]
a
r
e
m
a
c
hi
ne
le
a
r
ni
ng
m
ode
ls
th
a
t
c
r
e
a
te
or
ig
in
a
l
da
t
a
th
a
t
c
ons
is
t
of
two
s
ub
-
ne
twor
ks
,
a
ge
ne
r
a
to
r
a
nd
a
di
s
c
r
im
in
a
to
r
.
T
h
e
s
e
ne
twor
ks
c
om
pe
te
a
dve
r
s
a
r
ia
ll
y
th
r
oughout
th
e
tr
a
in
in
g
pe
r
io
d.
H
e
r
e
,
th
e
ge
ne
r
a
to
r
pr
oduc
e
s
a
r
ti
f
ic
ia
l
im
a
ge
s
a
nd
th
e
di
s
c
r
im
in
a
to
r
c
a
te
gor
iz
e
s
th
e
m
a
s
r
e
a
l
or
f
a
ke
.
T
he
ge
ne
r
a
to
r
ta
ke
s
f
e
e
dba
c
k
f
r
om
th
e
di
s
c
r
im
in
a
to
r
a
nd
m
a
k
e
s
it
e
r
a
ti
ve
i
m
pr
ove
m
e
nt
s
,
in
w
hi
c
h
m
a
nne
r
th
e
two
s
ub
-
ne
twor
ks
c
ha
ll
e
nge
e
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c
h
ot
he
r
to
c
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e
a
te
uni
que
d
a
ta
.
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r
e
ha
ve
be
e
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s
e
ve
r
a
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r
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f
in
e
m
e
nt
s
to
th
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or
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in
a
l
G
A
N
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pr
ove
th
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qua
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ty
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li
s
m
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im
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[
3]
a
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ti
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a
dve
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s
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k
(
D
C
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A
N
)
[
4]
.
S
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m
ode
ls
li
ke
a
tt
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na
l
ge
ne
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ti
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dve
r
s
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l
ne
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ks
(
A
tt
nG
A
N
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[
5
]
a
nd
s
ta
c
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d
ge
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r
a
ti
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a
dve
r
s
a
r
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l
ne
twor
ks
(
S
ta
c
kG
A
N
)
[
6]
a
r
e
popula
r
i
n
te
xt
-
to
-
im
a
ge
ge
ne
r
a
ti
on
f
or
b
ir
ds
a
nd othe
r
obj
e
c
ts
but
do
not
ge
ne
r
a
li
z
e
w
e
ll
a
nd
ha
ve
th
e
nua
nc
e
ne
e
d
e
d
f
or
f
a
c
e
s
ynt
he
s
i
s
.
S
ty
le
-
b
a
s
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d
ge
ne
r
a
ti
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a
dve
r
s
a
r
ia
l
n
e
twor
ks
(
S
ty
le
G
A
N
)
[
7]
,
ba
s
e
d
on
pr
ogr
e
s
s
iv
e
gr
ow
in
g
of
ge
n
e
r
a
ti
ve
a
dve
r
s
a
r
ia
l
n
e
twor
k
(
P
r
oG
A
N
)
[
8]
,
is
a
s
ta
te
-
of
-
th
e
-
a
r
t
ge
ne
r
a
ti
ve
m
ode
l
th
a
t
c
r
e
a
te
s
hi
gh
-
qua
li
ty
,
r
e
a
li
s
ti
c
im
a
ge
s
.
I
n S
ty
le
G
A
N
,
th
e
ne
twor
k
c
ont
a
in
s
m
a
ny
la
ye
r
s
,
w
it
h
th
e
in
it
ia
l
one
s
pr
oduc
in
g
a
lo
w
e
r
-
di
m
e
ns
io
na
l
im
a
ge
th
a
t
c
onc
e
nt
r
a
te
s
on
th
e
ba
s
ic
f
e
a
tu
r
e
s
a
nd
th
e
ot
he
r
l
a
ye
r
s
f
oc
us
e
d on a
ddi
ng mor
e
c
om
pl
e
x de
ta
il
s
t
o t
he
i
m
a
ge
. A
dva
nc
e
m
e
nt
s
i
n S
ty
le
G
A
N
ha
ve
s
e
e
n t
he
de
ve
lo
pm
e
nt
of
S
ty
le
G
A
N
2
[
9]
a
nd
S
ty
le
G
A
N
3
[
10]
.
A
ll
t
he
s
e
S
ty
le
G
A
N
m
ode
ls
r
e
qui
r
e
a
n
e
nor
m
ous
da
ta
s
e
t
f
or
tr
a
in
in
g,
w
hi
c
h
is
e
xt
r
e
m
e
ly
e
xpe
ns
iv
e
a
nd
c
o
m
put
a
ti
ona
ll
y
in
te
ns
iv
e
.
S
ty
le
G
A
N
2
-
a
da
pt
iv
e
di
s
c
r
im
in
a
to
r
a
ugm
e
nt
a
ti
on
(
A
DA
)
[
11]
w
a
s
c
r
e
a
te
d
to
f
ix
th
is
pr
obl
e
m
a
nd
c
a
n
be
tr
a
in
e
d
on
a
s
m
a
ll
e
r
,
li
m
it
e
d
da
ta
s
e
t.
I
t
bui
ld
s
on
th
e
or
ig
in
a
l
S
ty
le
G
A
N
a
r
c
hi
te
c
t
ur
e
by
a
ddi
ng
da
t
a
a
ugm
e
nt
a
ti
on
te
c
hni
que
s
.
S
ty
le
G
A
N
2
w
a
s
us
e
d
w
it
h
te
xt
e
n
c
ode
r
s
li
ke
B
E
R
T
in
‘
te
xt
t
o
f
a
c
e
ge
n
e
r
a
ti
on
w
it
h
S
ty
le
G
A
N
2’
[
12]
,
a
nd
D
is
ti
lB
e
r
t
in
‘
S
ty
le
T
2F
’
[
13]
to
ge
ne
r
a
te
f
a
c
e
s
f
r
om
t
e
xt
de
s
c
r
ip
ti
ons
.
A
v
is
u
a
l
-
li
ng
ui
s
ti
c
m
od
e
l
c
a
l
le
d
c
o
nt
r
a
s
ti
v
e
la
ng
ua
ge
-
im
a
g
e
pr
e
-
tr
a
i
ni
n
g
(
C
L
I
P
)
[
1
4]
w
a
s
c
r
e
a
t
e
d
by
O
p
e
nA
I
to
li
n
k
t
e
x
ts
w
i
th
i
m
a
ge
s
.
I
t
is
a
de
e
p
le
a
r
ni
n
g
m
od
e
l
tr
a
in
e
d
on
40
0
m
i
ll
i
on
i
m
a
ge
-
te
xt
pa
ir
s
ob
ta
in
e
d
f
r
om
I
m
a
g
e
N
e
t
a
n
d
c
on
ne
c
t
s
t
h
e
m
b
y e
nc
odi
ng
bot
h i
nt
o
a
j
oi
n
t
e
m
b
e
d
di
n
g
s
p
a
c
e
.
I
t
i
s
n
ot
a
bl
e
f
or
i
t
s
s
u
c
c
e
s
s
i
n
z
e
r
o
-
s
ho
t
l
e
a
r
ni
ng,
w
h
ic
h
i
nvo
lv
e
s
c
la
s
s
if
yi
n
g
im
a
ge
s
w
it
h
l
a
b
e
l
s
n
ot
e
n
c
o
unt
e
r
e
d i
n
tr
a
i
ni
n
g. C
L
I
P
c
o
nt
a
in
s
a
n
im
a
ge
a
n
d
t
e
x
t
e
nc
od
e
r
a
nd
w
a
s
u
s
e
d
w
it
h
S
ty
le
G
A
N
i
n
s
e
v
e
r
a
l
t
e
xt
-
to
-
f
a
c
e
ge
ne
r
a
t
io
n
a
nd
m
a
n
ip
ul
a
ti
o
n
m
od
e
l
s
li
ke
S
ty
l
e
C
L
I
P
[
1
5]
a
n
d
T
e
di
G
A
N
[
16]
.
P
r
e
vi
ou
s
w
o
r
ks
ha
ve
e
x
pl
or
e
d
t
e
x
t
-
to
-
f
a
c
e
ge
n
e
r
a
ti
o
n
a
nd
m
a
n
ip
u
la
ti
o
n
u
s
in
g
a
pr
e
-
tr
a
in
e
d
S
ty
l
e
G
A
N
[
7
]
a
n
d
C
L
I
P
m
o
de
l
[
1
4]
.
C
L
I
P
D
r
a
w
[
17]
i
s
a
t
e
xt
-
to
-
dr
a
w
i
ng
a
lg
or
it
hm
t
ha
t
s
yn
th
e
s
iz
e
s
dr
a
w
in
gs
by
m
a
xi
m
i
z
i
ng
t
he
c
os
in
e
s
im
il
a
r
it
y
b
e
t
w
e
e
n
a
g
e
n
e
r
a
t
e
d
s
k
e
t
c
h
a
n
d
a
n
in
pu
t
de
s
c
r
i
pt
i
on.
T
h
is
m
e
th
od
i
s
bi
a
s
e
d
t
ow
a
r
d
s
dr
a
w
in
gs
r
a
th
e
r
t
ha
n
r
e
a
li
s
t
ic
im
a
g
e
s
.
R
e
d
dy
e
t
al
.
[
1
8]
pr
op
o
s
e
d
a
m
od
e
l
th
a
t
ge
n
e
r
a
te
d
a
r
a
n
dom
i
m
a
g
e
a
n
d
opt
im
i
z
e
d
i
ts
l
a
te
nt
c
od
e
w
it
h
C
L
I
P
’
s
l
o
s
s
f
u
nc
ti
o
n.
R
e
c
e
nt
ly
,
F
a
c
e
b
oo
k R
e
s
e
a
r
c
h
r
e
l
e
a
s
e
d M
e
ta
C
L
I
P
[
1
9]
,
b
a
s
e
d on
C
L
I
P
, t
r
a
in
e
d
on
a
m
a
s
s
iv
e
d
a
t
a
s
e
t
of
1
bi
ll
i
on
i
m
a
ge
s
f
e
t
c
h
e
d
f
r
om
C
om
m
on
C
r
a
w
l
. C
L
I
P
’
s
a
c
c
om
pl
i
s
hm
e
nt
s
w
e
r
e
s
a
id
t
o
l
ie
in
t
h
e
q
ua
li
t
y
of
da
ta
it
w
a
s
tr
a
i
ne
d
on
a
nd
not
in
it
s
a
r
c
hi
t
e
c
tu
r
e
.
S
in
c
e
th
e
r
e
i
s
i
na
d
e
qu
a
t
e
i
nf
or
m
a
t
io
n
on
h
ow
C
L
I
P
o
bt
a
in
e
d
i
t
s
tr
a
i
ni
n
g
d
a
t
a
,
M
e
t
a
C
L
I
P
i
nt
e
nd
e
d
to
un
ve
il
a
n
d
r
e
f
in
e
C
L
I
P
’
s
m
e
th
od of
a
c
qui
r
in
g
d
a
t
a
. M
e
t
a
C
L
I
P
i
s
r
e
l
a
ti
ve
ly
ne
w
a
n
d
ha
s
no
t
ha
d
a
n
y
a
p
pl
i
c
a
ti
on
s
in
t
e
xt
-
to
-
im
a
g
e
s
y
nt
he
s
i
s
.
V
i
s
io
n
-
l
a
n
gu
a
g
e
m
od
e
l
s
a
nd
C
L
I
P
,
in
pa
r
t
ic
ul
a
r
,
a
r
e
c
r
uc
ia
l
m
od
e
l
s
in
a
r
ti
f
ic
ia
l
i
nt
e
ll
ig
e
n
c
e
t
ha
t
ha
ve
m
a
d
e
a
c
o
n
s
id
e
r
a
bl
e
im
pa
c
t
i
n
t
he
f
i
e
l
d.
H
ow
e
v
e
r
, m
o
s
t
f
oc
us
onl
y
on
E
ngl
is
h
t
e
xt
s
, w
h
ic
h i
s
a
c
on
s
e
q
ue
nc
e
of
t
he
s
c
a
r
c
e
num
be
r
of
im
a
g
e
-
t
e
x
t
d
a
t
a
s
e
t
s
in
ot
h
e
r
l
a
n
gu
a
g
e
s
.
M
ul
ti
li
ng
ua
l
-
C
L
I
P
[
20]
w
a
s
in
tr
od
u
c
e
d
to
a
d
dr
e
s
s
th
i
s
l
im
it
a
t
io
n
a
n
d
le
ve
r
a
g
e
d
th
e
s
t
r
e
ngt
h
of
C
L
I
P
’
s
pr
e
-
tr
a
i
ne
d
t
e
x
t
e
n
c
o
de
r
to
tr
a
in
a
s
tu
d
e
n
t
m
od
e
l
to
pr
o
c
e
s
s
m
ul
t
ip
l
e
la
ngu
a
g
e
s
.
I
t
ha
s
s
e
v
e
r
a
l
pr
e
-
t
r
a
in
e
d
m
od
e
l
s
d
e
s
ig
ne
d f
or
d
iv
e
r
s
e
l
a
ng
ua
g
e
s
a
nd
c
a
n
b
e
us
e
d
in
m
u
lt
il
in
g
u
a
l
t
e
xt
-
to
-
f
a
c
e
g
e
ne
r
a
ti
on
.
E
a
c
h
of
th
e
s
e
m
ode
ls
ha
s
it
s
a
dva
nt
a
ge
s
a
nd
di
s
a
dva
nt
a
g
e
s
.
O
ur
m
a
in
c
ont
r
ib
ut
io
n
in
vol
ve
s
e
xpe
r
im
e
nt
in
g,
a
na
ly
z
in
g,
a
nd
c
om
pa
r
in
g
C
L
I
P
,
M
e
ta
C
L
I
P
,
a
nd
m
ul
ti
li
ngua
l
-
C
L
I
P
in
E
ngl
is
h
a
nd
T
a
m
il
in
te
xt
-
to
-
f
a
c
e
ge
ne
r
a
ti
on. M
ul
ti
li
ngua
l
-
C
L
I
P
c
a
n be
us
e
d t
o e
na
bl
e
hi
gh
-
qua
li
ty
te
xt
-
to
-
f
a
c
e
s
ynt
he
s
is
i
n s
e
ve
r
a
l
ot
he
r
la
ngua
ge
s
to
m
a
ke
it
a
c
c
e
s
s
ib
le
w
or
ld
w
id
e
.
W
e
a
ls
o
a
s
s
e
s
s
th
e
p
e
r
f
or
m
a
nc
e
of
a
c
us
to
m
S
ty
le
G
A
N
2
-
A
DA
m
ode
l
[
11]
,
tr
a
in
e
d
w
it
h
a
s
ubs
e
t
of
im
a
ge
s
f
r
om
t
he
F
a
ir
F
a
c
e
da
ta
s
e
t
[
21]
,
a
nd
a
p
r
e
-
tr
a
in
e
d
S
ty
le
G
A
N
2
m
ode
l
tr
a
in
e
d
w
it
h
th
e
F
F
H
Q
da
ta
s
e
t.
O
ur
goa
l
is
to
s
ynt
he
s
iz
e
r
e
a
li
s
ti
c
im
a
ge
s
th
a
t
c
ont
a
in
th
e
f
in
e
-
gr
a
in
e
d
a
tt
r
ib
ut
e
s
m
e
nt
io
ne
d
in
th
e
de
s
c
r
ip
ti
on.
O
ur
pr
opos
e
d
s
y
s
te
m
in
te
gr
a
te
s
C
L
I
P
a
nd
it
s
va
r
ia
nt
s
w
it
h
S
ty
le
G
A
N
2
or
S
ty
le
G
A
N
2
-
A
DA
.
C
L
I
P
[
14]
,
a
lo
ng
w
it
h
it
s
va
r
ia
nt
s
,
M
e
ta
C
L
I
P
[
19]
a
nd
m
ul
ti
li
ngua
l
-
C
L
I
P
[
20
]
, c
onne
c
t
te
xt
de
s
c
r
ip
ti
ons
w
it
h
th
e
ir
vi
s
ua
l
r
e
pr
e
s
e
nt
a
ti
ons
, w
hi
le
S
ty
le
G
A
N
2 o
r
S
ty
le
G
A
N
2
-
A
DA
[
11]
ge
ne
r
a
te
s
th
e
hi
gh
-
qua
li
ty
im
a
ge
s
.
S
ty
le
G
A
N
2
-
A
DA
w
it
h
th
e
F
a
ir
F
a
c
e
da
ta
s
e
t
is
us
e
d
to
e
ns
ur
e
di
ve
r
s
it
y
a
nd
f
a
ir
ne
s
s
in
th
e
s
ynt
he
s
iz
e
d
im
a
ge
s
a
nd
c
a
n
be
c
ont
r
a
s
te
d
w
it
h
th
e
pr
e
-
tr
a
in
e
d
S
ty
le
G
A
N
2
m
ode
l.
T
he
ove
r
a
ll
a
lg
or
it
hm
ope
r
a
te
s
in
two
pha
s
e
s
.
I
n
th
e
f
i
r
s
t
pha
s
e
,
a
gi
ve
n
te
xt
de
s
c
r
ip
ti
on
is
e
nc
ode
d
us
in
g
C
L
I
P
or
it
s
va
r
ia
nt
s
te
xt
e
nc
ode
r
[
8]
,
[
14]
,
[
19
]
.
A
s
ta
r
ti
ng
la
te
nt
ve
c
to
r
,
w
hi
c
h
is
a
num
e
r
ic
a
l
r
e
pr
e
s
e
nt
a
ti
on
of
th
e
f
e
a
tu
r
e
s
of
a
te
xt
or
im
a
ge
in
a
hi
ghe
r
d
im
e
ns
io
na
l
la
te
nt
s
pa
c
e
,
is
ge
n
e
r
a
te
d.
T
hi
s
i
s
done
by r
a
ndoml
y s
ynt
he
s
iz
in
g a
f
e
w
i
m
a
ge
s
, s
to
r
in
g t
he
l
os
s
b
e
twe
e
n t
he
e
nc
ode
d ge
ne
r
a
te
d i
m
a
ge
a
nd t
e
xt
,
s
e
le
c
ti
ng
th
e
im
a
ge
w
it
h
th
e
lo
w
e
s
t
lo
s
s
,
a
nd
obt
a
in
in
g
i
ts
la
te
nt
ve
c
to
r
a
s
th
e
s
ta
r
ti
ng
la
te
nt
c
ode
.
I
n
th
e
s
e
c
ond
ph
a
s
e
,
th
is
s
ta
r
ti
ng
la
te
nt
c
od
e
is
opt
im
iz
e
d
w
it
h a
lo
s
s
r
e
pe
a
te
dl
y. T
he
lo
s
s
i
s
f
ound
in
e
ve
r
y
it
e
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t
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is
to
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H
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r
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it
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pe
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ubpa
r
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la
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r
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m
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in
r
e
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li
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F
in
a
ll
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w
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pr
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us
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DA
m
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ve
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it
y
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a
ir
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s
s
in
it
s
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s
iz
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im
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g
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s
.
T
hi
s
r
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s
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a
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c
h
pa
p
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r
in
te
nds
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pr
ovi
de
us
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t
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lp
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m
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ki
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f
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m
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t
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t
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f
it
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I
n
s
e
c
ti
on
2,
a
li
te
r
a
tu
r
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s
ur
ve
y
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c
onduc
t
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d
th
a
t
goe
s
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e
r
r
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la
te
d
w
or
k,
r
e
vi
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w
in
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va
r
io
us
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im
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f
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ti
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ode
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S
e
c
ti
on
3
di
s
c
us
s
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th
e
m
odul
e
s
us
e
d
in
our
a
lg
or
it
hm
,
S
ty
le
G
A
N
[
7]
,
[
9]
–
[
11]
,
C
L
I
P
,
M
e
ta
C
L
I
P
,
a
nd
m
ul
ti
li
ngua
l
-
C
L
I
P
,
our
pr
opos
e
d
a
r
c
hi
te
c
tu
r
e
,
w
hi
c
h
c
on
s
is
ts
of
th
e
da
ta
s
e
t
de
s
c
r
ip
ti
on,
ove
r
a
ll
a
r
c
hi
te
c
tu
r
e
,
a
lg
or
it
h
m
,
e
xpl
a
na
ti
on
of
our
lo
s
s
f
unc
ti
on
a
nd
th
e
e
va
lu
a
ti
on
m
e
tr
ic
s
,
a
nd
f
in
a
ll
y
th
e
e
xpe
r
im
e
nt
s
w
e
c
onduc
te
d.
S
e
c
ti
on
4
c
ont
a
in
s
th
e
r
e
s
ul
ts
a
nd
a
c
om
pr
e
he
ns
iv
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di
s
c
us
s
io
n
s
e
c
ti
on
w
he
r
e
th
e
r
e
s
ul
ts
a
r
e
pr
e
s
e
nt
e
d
a
nd
s
tu
d
ie
d.
F
in
a
ll
y,
w
e
pr
e
s
e
nt
our
c
onc
lu
s
io
ns
in
s
e
c
ti
on
5 a
nd dis
c
us
s
f
ut
ur
e
w
or
k.
2.
L
I
T
E
R
A
T
U
R
E
S
U
R
V
E
Y
T
e
xt
-
to
-
f
a
c
e
a
nd
te
xt
-
to
-
im
a
ge
s
ynt
he
s
is
us
in
g
G
A
N
s
a
r
e
w
e
ll
-
r
e
s
e
a
r
c
he
d
to
pi
c
s
of
g
e
ne
r
a
ti
ve
A
I
th
a
t
ha
ve
la
r
ge
-
s
c
a
le
a
ppl
ic
a
ti
ons
.
S
om
e
of
th
e
m
os
t
s
ig
ni
f
ic
a
nt
pa
pe
r
s
a
r
e
out
li
ne
d
a
lo
ng
w
it
h
th
e
ir
c
ont
r
ib
ut
io
ns
.
Z
ha
ng
e
t
al
.
[
6]
in
tr
oduc
e
d
one
of
th
e
f
ir
s
t
te
xt
-
to
-
im
a
ge
m
ode
ls
c
a
ll
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d
S
ta
c
kG
A
N
,
tr
a
in
e
d
on
bi
r
d
im
a
ge
s
f
r
om
th
e
C
U
B
a
nd
M
S
C
O
C
O
da
ta
s
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ts
.
T
h
e
m
ode
l
ha
d
two
G
A
N
s
s
ta
c
ke
d
on
to
p
of
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c
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he
r
,
w
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e
th
e
f
ir
s
t
G
A
N
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dde
d
pr
im
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r
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ha
r
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f
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tu
r
e
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ke
s
ha
pe
a
nd
c
ol
or
,
a
nd
th
e
s
e
c
ond
one
a
dde
d
m
or
e
hi
gh
-
le
ve
l
f
e
a
tu
r
e
s
.
T
hi
s
m
ode
l
ut
il
iz
e
d
th
e
or
ig
in
a
l
G
A
N
,
w
hi
c
h
le
d
to
i
s
s
ue
s
li
ke
m
ode
c
ol
la
ps
e
a
nd
tr
a
in
in
g
in
s
ta
bi
li
ty
.
X
u
e
t
al
.
[
5]
in
t
r
oduc
e
d
A
tt
n
-
G
A
N
,
a
ne
w
e
r
a
r
c
hi
te
c
tu
r
e
w
it
hi
n
th
e
G
A
N
f
r
a
m
e
w
or
k
th
a
t
us
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d
th
e
s
a
m
e
da
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t
a
s
S
ta
c
kG
A
N
a
nd
f
ol
lo
w
e
d
a
m
ul
ti
pl
e
-
s
ta
ge
a
ppr
oa
c
h.
I
n
e
ve
r
y s
ta
ge
,
a
f
e
w
s
ig
ni
f
ic
a
nt
ke
yw
or
ds
f
r
om
th
e
in
put
p
r
om
pt
w
e
r
e
e
xt
r
a
c
te
d
a
nd
us
e
d
to
s
ynt
he
s
iz
e
a
n
im
a
ge
of
lo
w
r
e
s
ol
ut
io
n,
w
hi
c
h
w
a
s
f
in
a
ll
y
c
om
bi
ne
d
u
s
in
g
th
e
w
or
d
c
ont
e
xt
s
de
v
e
lo
pe
d
in
th
e
pr
e
vi
ous
s
ta
g
e
s
.
T
he
p
e
r
f
or
m
a
nc
e
of
th
is
m
ode
l
de
te
r
io
r
a
te
d a
s
t
he
de
s
c
r
ip
ti
on got l
onge
r
be
c
a
us
e
t
he
a
tt
e
nt
io
n m
a
p be
c
a
m
e
m
or
e
c
om
pl
ic
a
te
d t
o t
r
a
in
.
N
a
s
ir
e
t
al
.
[
22]
pr
opos
e
d
T
e
xt
2F
a
c
e
G
a
n
,
w
he
r
e
th
e
m
a
in
c
o
nt
r
ib
ut
io
n
la
y
in
c
r
e
a
ti
ng
a
n
a
lg
or
it
hm
to
a
dd
c
a
pt
io
ns
to
th
e
im
a
ge
s
of
th
e
C
e
le
b
A
da
ta
s
e
t,
de
s
c
r
ib
in
g
th
e
a
tt
r
ib
ut
e
s
th
a
t
w
e
r
e
pr
e
s
e
nt
in
th
e
m
.
T
hi
s
w
a
s
a
c
hi
e
ve
d
u
s
in
g
a
s
ki
p
-
th
ought
e
nc
ode
r
a
nd
w
a
s
a
s
ig
ni
f
ic
a
nt
c
o
nt
r
ib
ut
io
n
to
th
is
f
ie
ld
,
a
s
e
a
r
li
e
r
f
a
c
ia
l
im
a
g
e
-
te
xt
da
ta
s
e
ts
w
e
r
e
s
c
a
r
c
e
.
S
a
b
a
e
e
t
al
.
[
13]
i
nt
r
odu
c
e
d
S
t
yl
e
T
2F
u
s
i
ng
D
is
ti
l
B
e
r
t
to
e
xt
r
a
c
t
f
a
c
ia
l
f
e
a
tu
r
e
s
f
r
om
a
t
e
xt
d
e
s
c
r
ip
t
io
n
,
w
hi
c
h
w
e
r
e
tr
a
n
s
f
or
m
e
d
i
nt
o
a
f
in
a
l
l
a
t
e
n
t
v
e
c
to
r
p
a
s
s
e
d
to
th
e
S
ty
l
e
G
A
N
2
g
e
n
e
r
a
to
r
.
F
e
a
tu
r
e
di
r
e
c
t
io
ns
w
e
r
e
us
e
d
to
n
a
vi
ga
te
th
e
S
ty
le
G
A
N
2
la
te
nt
s
pa
c
e
a
nd
r
e
a
c
h
th
e
r
e
qui
r
e
d
la
te
nt
v
e
c
t
or
.
T
hi
s
m
o
de
l
h
a
d
m
u
lt
i
pl
e
pr
ob
le
m
s
du
e
t
o
e
nt
a
ng
le
d
f
e
a
t
ur
e
di
r
e
c
ti
on
s
.
F
ol
l
ow
i
ng
a
s
i
m
il
a
r
a
ppr
oa
c
h
,
A
ya
nt
h
i
a
nd
M
u
na
s
i
ng
he
[
12]
pr
opos
e
d
a
f
r
a
m
e
w
or
k
th
a
t
e
xhi
bi
te
d
a
s
im
il
a
r
it
y
of
ove
r
50
%
t
o
th
e
or
ig
in
a
l
im
a
ge
s
.
T
he
m
ode
l
us
e
d
B
E
R
T
to
e
xt
r
a
c
t
th
e
te
xt
e
nc
odi
ngs
,
w
hi
c
h
w
a
s
gi
ve
n
to
a
pr
e
-
tr
a
in
e
d
G
A
N
s
uc
h
a
s
S
ty
le
G
A
N
2.
I
t
w
a
s
tr
a
in
e
d
w
it
h
th
e
pe
r
c
e
pt
ua
l
lo
s
s
f
unc
ti
on
a
nd
p
e
r
f
or
m
e
d
be
tt
e
r
th
a
n
ol
de
r
G
A
N
s
li
ke
A
tt
nG
A
N
[
5]
,
a
nd
S
ta
c
kG
A
N
.
H
ow
e
ve
r
,
th
e
da
ta
s
e
t
us
e
d
in
th
is
pa
pe
r
c
ons
is
te
d
of
onl
y
5685
im
a
ge
-
te
xt
pa
ir
s
a
nd
he
nc
e
di
d
not
ge
ne
r
a
li
z
e
w
e
ll
,
le
a
di
ng
to
ove
r
f
it
ti
ng.
T
odm
a
l
e
t
al
.
[
23]
pr
opos
e
d
two
m
e
th
ods
th
a
t
u
s
e
d
C
L
I
P
,
S
ty
le
G
A
N
,
a
nd
th
e
pi
xe
l2
s
ty
le
2pi
xe
l
im
a
ge
e
nc
ode
r
[
24]
,
w
hi
c
h
pr
oj
e
c
ts
im
a
ge
s
in
to
th
e
e
xt
e
nde
d
la
te
nt
s
pa
c
e
of
S
ty
le
G
A
N
.
T
he
f
ir
s
t
m
e
th
od
m
a
ppe
d
a
pr
om
pt
to
th
e
e
xt
e
nd
e
d
la
te
nt
s
pa
c
e
of
S
ty
le
G
A
N
,
w
hi
le
th
e
s
e
c
ond
m
a
ppe
d
it
to
th
e
in
it
ia
l
la
te
nt
s
pa
c
e
of
S
ty
le
G
A
N
.
T
he
f
ir
s
t
m
e
th
od
r
e
s
ul
te
d
in
f
a
c
ia
l
im
a
ge
s
th
a
t
w
e
r
e
tr
ue
to
th
e
de
s
c
r
ip
ti
on
but
le
s
s
li
f
e
li
ke
,
w
hi
le
th
e
s
e
c
ond
ha
d
m
or
e
r
e
a
li
s
t
ic
im
a
ge
s
but
le
s
s
c
ont
r
ol
ove
r
th
e
a
tt
r
ib
u
te
s
.
R
e
ddy
e
t
al
.
[
18]
pr
opos
e
d a
n a
lg
or
i
th
m
t
ha
t
t
r
a
in
e
d a
S
ty
le
G
A
N
i
nve
r
te
r
t
o
e
nc
ode
a
gi
ve
n i
m
a
ge
a
nd obta
i
n
it
s
in
te
r
m
e
di
a
te
la
te
nt
c
ode
a
nd
ut
i
li
z
e
d
a
lo
ope
d
ne
twor
k
f
or
te
xt
-
to
-
im
a
ge
ge
ne
r
a
ti
on.
I
ni
ti
a
ll
y,
a
r
a
ndo
m
la
te
nt
c
ode
is
c
r
e
a
te
d
a
nd
pa
s
s
e
d
to
th
e
S
ty
le
G
A
N
ge
ne
r
a
to
r
to
s
ynt
he
s
iz
e
a
r
a
ndom
im
a
ge
.
T
he
s
ynt
he
s
iz
e
d
im
a
ge
a
nd
in
put
c
a
pt
io
n
a
r
e
c
om
pa
r
e
d
w
it
h
C
L
I
P
L
os
s
.
T
he
tr
a
in
e
d
S
ty
le
G
A
N
in
ve
r
te
r
f
in
ds
th
e
la
te
nt
c
ode
of
th
e
s
ynt
he
s
iz
e
d
im
a
ge
,
upda
t
e
s
it
w
it
h
th
e
lo
s
s
,
a
nd
r
e
ge
ne
r
a
te
s
a
n
im
a
ge
.
T
hi
s
pr
oc
e
s
s
oc
c
ur
s
f
or
a
f
ix
e
d
num
be
r
of
s
te
ps
a
s
th
e
lo
s
s
va
lu
e
de
c
r
e
a
s
e
s
.
I
n
th
e
br
a
nc
h
o
f
te
xt
-
to
-
f
a
c
e
ge
ne
r
a
ti
on
in
m
ul
ti
pl
e
la
ngua
ge
s
,
L
i
e
t
al
.
[
25]
pr
opos
e
d
a
m
ode
l
th
a
t
us
e
d
tr
a
ns
f
e
r
le
a
r
ni
ng
a
nd ma
de
us
e
of
ne
ur
a
l
m
a
c
hi
ne
tr
a
ns
la
ti
on.
I
t
ha
d
two
a
ppr
oa
c
he
s
a
nd
te
s
te
d
th
e
r
e
s
ul
ts
on
th
e
C
U
B
a
nd
C
O
C
O
-
C
N
da
ta
s
e
ts
w
it
h
S
ty
le
G
A
N
2.
I
t
a
na
ly
z
e
d
a
nd
e
va
lu
a
te
d how the
ir
c
r
os
s
-
li
ngua
l
tr
a
ns
f
e
r
m
e
th
ods
c
om
p
a
r
e
t
o ot
he
r
t
r
a
ns
f
e
r
m
e
th
ods
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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F
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t
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in
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(
P
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3591
3.
M
E
T
H
O
D
S
A
N
D
C
O
M
P
O
N
E
N
T
S
3.1. S
t
yl
e
G
A
N
S
ty
le
G
A
N
, bui
l
t
on t
op
of
P
r
oG
A
N
[
8]
, i
s
t
he
s
ta
t
e
-
of
-
th
e
-
a
r
t
m
ode
l
in
t
h
e
f
ie
l
d of
g
e
ne
r
a
t
iv
e
A
I
us
in
g
G
A
N
s
,
t
ha
t
i
s
c
a
p
a
bl
e
of
pr
od
uc
in
g
hi
gh
-
qu
a
li
ty
,
r
e
a
li
s
t
ic
im
a
ge
s
.
U
nl
ik
e
it
s
pr
e
d
e
c
e
s
s
or
s
,
w
hi
c
h
us
e
d
onl
y
one
la
t
e
nt
s
p
a
c
e
c
a
ll
e
d
th
e
Z
s
p
a
c
e
t
o s
a
m
pl
e
a
t
tr
ib
ut
e
s
f
r
om
,
S
t
yl
e
G
A
N
pr
opo
s
e
d
to
u
s
e
a
n
in
te
r
m
e
di
a
t
e
l
a
te
nt
s
pa
c
e
c
a
ll
e
d
th
e
W
s
p
a
c
e
.
T
h
e
Z
s
p
a
c
e
c
ons
is
t
s
of
r
a
ndo
m
ve
c
t
or
s
(
n
oi
s
e
v
e
c
to
r
s
)
th
a
t
c
o
nt
r
ol
im
a
ge
ge
ne
r
a
ti
on.
I
n
c
ont
r
a
s
t,
th
e
W
s
pa
c
e
,
w
hi
c
h
w
e
ge
t
b
y
pa
s
s
i
n
g
th
e
in
put
th
r
o
ugh
a
n
8
-
la
y
e
r
M
L
P
m
a
ppi
ng
ne
twor
k
,
e
n
a
bl
e
s
s
m
oot
h
e
r
in
te
r
p
ol
a
ti
on
be
t
w
e
e
n
la
t
e
nt
v
e
c
to
r
s
.
I
t
e
n
s
ur
e
d
m
or
e
di
s
e
nt
a
n
gl
e
m
e
nt
be
twe
e
n
di
f
f
e
r
e
nt
f
e
a
tu
r
e
s
in
th
e
W
s
p
a
c
e
,
w
hi
c
h
pr
ovi
d
e
s
m
or
e
c
ont
r
o
l
ove
r
th
e
in
di
vi
du
a
l
a
tt
r
ib
ut
e
s
.
S
ty
le
G
A
N
w
a
s
s
uc
c
e
e
de
d
by
S
ty
le
G
A
N
2
a
nd
S
t
yl
e
G
A
N
3
[
10]
,
w
hi
c
h
ha
d
a
r
c
hi
te
c
tu
r
a
l
c
h
a
ng
e
s
t
o
c
o
m
ba
t
th
e
li
m
it
a
ti
on
s
pos
e
d by the
or
ig
in
a
l
S
ty
le
G
A
N
.
T
h
e
s
e
m
odi
f
ic
a
ti
on
s
f
ix
e
d e
a
r
l
ie
r
i
s
s
ue
s
l
ik
e
pha
s
e
a
r
ti
f
a
c
ts
, t
h
e
w
a
te
r
-
dr
opl
e
t
e
f
f
e
c
t,
a
nd
t
e
xt
ur
e
s
ti
c
ki
ng,
w
hi
c
h
ge
n
e
r
a
t
e
d
e
v
e
n
m
or
e
r
e
a
li
s
ti
c
im
a
g
e
s
.
A
no
th
e
r
m
a
jo
r
d
e
ve
lo
pm
e
nt
w
a
s
th
e
S
ty
le
G
A
N
2
-
A
DA
m
ode
l
,
w
hi
c
h
h
a
ndl
e
d
th
e
pr
o
bl
e
m
of
li
m
it
e
d
da
t
a
s
e
ts
.
M
o
s
t
G
A
N
-
b
a
s
e
d
m
od
e
ls
r
e
qui
r
e
50,000
-
100,
000
im
a
g
e
s
f
or
tr
a
in
in
g
a
nd
us
e
a
ugm
e
n
ta
ti
o
n
t
o
w
or
k
w
it
h
s
m
a
ll
d
a
ta
s
e
t
s
,
w
hi
c
h
r
e
s
ul
t
s
in
ove
r
f
it
ti
ng
. T
hi
s
c
ha
l
le
ng
e
,
a
ddr
e
s
s
e
d b
y t
he
S
t
yl
e
G
A
N
2
-
A
DA
m
ode
l,
pr
oduc
e
d ou
tp
ut
s
s
im
il
a
r
t
o S
t
yl
e
G
A
N
2
w
hi
le
us
in
g a
s
ig
ni
f
ic
a
nt
ly
s
m
a
ll
e
r
d
a
ta
s
e
t,
w
h
ic
h
a
s
s
i
s
te
d i
n o
ve
r
c
om
in
g
da
ta
s
c
a
r
c
it
y
c
ha
ll
e
ng
e
s
.
3.2. Con
t
r
as
t
iv
e
l
an
gu
age
-
im
age
p
r
e
-
t
r
ai
n
in
g
C
L
I
P
i
s
a
m
o
d
e
l
,
d
e
s
i
g
n
e
d
t
o
l
i
n
k
a
s
e
t
of
pi
c
t
ur
e
s
t
o
t
e
x
t
d
e
s
c
r
ip
t
i
o
n
s
,
t
r
a
i
n
e
d
on
4
0
0
m
il
l
i
on
p
a
ir
s
of
i
m
a
g
e
-
t
e
x
t
p
a
i
r
s
o
n
I
m
a
g
e
N
e
t
.
T
h
e
C
L
I
P
f
r
a
m
e
w
o
r
k
c
o
n
s
i
s
t
s
o
f
a
t
e
x
t
e
n
c
o
d
e
r
t
h
a
t
u
ti
l
i
z
e
s
t
r
a
n
s
f
o
r
m
e
r
s
a
nd
a
n
i
m
a
g
e
e
n
c
od
e
r
t
h
a
t
u
t
i
li
z
e
s
R
e
s
N
e
t
[
2
6
]
o
r
i
m
a
g
e
t
r
a
n
s
f
or
m
e
r
s
[
2
7]
.
I
t
e
m
p
l
o
y
s
a
m
e
tr
i
c
t
o
m
e
a
s
ur
e
t
h
e
l
ik
e
n
e
s
s
of
a
g
i
v
e
n c
a
p
ti
o
n t
o a
pi
c
t
ur
e
c
a
ll
e
d
c
o
s
i
n
e
s
i
m
i
l
a
r
it
y
. B
o
t
h t
h
e
e
n
c
od
e
r
s
yi
e
l
d u
n
if
o
r
m
-
s
i
z
e
d
e
m
b
e
d
d
i
ng
s
p
o
s
it
i
o
n
e
d
i
n
a
jo
i
n
t
e
m
b
e
d
di
n
g
s
p
a
c
e
.
I
t
i
s
po
s
s
i
b
l
e
t
o
f
i
n
d
h
o
w
f
a
r
a
p
a
r
t
t
he
e
n
c
o
d
e
d
c
a
pt
i
o
n
a
n
d
pi
c
t
ur
e
a
r
e
i
n
t
h
i
s
s
p
a
c
e
.
C
L
I
P
i
s
f
a
m
o
u
s
f
o
r
c
a
t
e
g
or
i
z
i
ng
a
n
i
m
a
g
e
w
i
t
h
l
a
b
e
l
s
n
ot
e
n
c
o
un
t
e
r
e
d
d
ur
i
ng
tr
a
i
n
in
g
,
c
a
ll
e
d
z
e
r
o
-
s
h
ot
l
e
a
r
n
i
ng
.
3.3. M
e
t
ad
at
a
-
c
u
r
at
e
d
l
an
gu
age
-
im
age
p
r
e
-
t
r
ai
n
in
g
F
a
c
e
b
ook R
e
s
e
a
r
c
h
d
e
ve
lo
pe
d
M
e
ta
C
L
I
P
,
w
hi
c
h
hi
g
hl
ig
ht
s
th
e
da
ta
c
ol
le
c
ti
on
m
e
th
od
of
C
L
I
P
s
in
c
e
it
s
u
gge
s
t
s
th
a
t
i
ns
ig
ht
in
to
th
is
pr
oc
e
s
s
c
a
n
r
e
v
e
a
l
th
e
f
a
c
to
r
s
t
ha
t
m
a
de
it
s
o
s
u
c
c
e
s
s
f
ul
.
S
in
c
e
C
L
I
P
do
e
s
not
di
s
c
l
os
e
how
it
c
ol
l
e
c
t
s
da
t
a
,
th
e
p
a
pe
r
ob
s
e
r
v
e
s
w
ha
t
qu
a
nt
if
ie
s
good
qu
a
li
ty
d
a
ta
a
nd dis
c
u
s
s
e
s
a
t
e
c
hni
que
to
r
e
ve
a
l
C
L
I
P
'
s
s
e
le
c
ti
on
pr
o
c
e
s
s
.
M
e
ta
C
L
I
P
ha
s
b
e
e
n
tr
a
in
e
d
w
i
th
1
bi
ll
io
n
im
a
ge
s
pr
e
s
e
nt
in
C
om
m
on
C
r
a
w
l
a
nd b
e
a
t
s
C
L
I
P
'
s
p
e
r
f
or
m
a
n
c
e
i
n i
m
a
ge
c
la
s
s
if
ic
a
ti
on
w
it
h u
nkn
ow
n l
a
be
l
s
,
c
a
ll
e
d
z
e
r
o
-
s
hot
l
e
a
r
ni
ng.
3.4. M
u
lt
il
in
gu
al
-
c
u
r
at
e
d
l
an
gu
age
-
im
age
p
r
e
-
t
r
ai
n
in
g
M
os
t
vi
s
io
n
la
ngua
ge
ne
twor
ks
a
r
e
c
e
nt
e
r
e
d
on
E
ngl
is
h
be
c
a
us
e
of
th
e
s
pa
r
s
e
num
be
r
of
i
m
a
ge
-
te
xt
da
ta
s
e
ts
a
v
a
il
a
bl
e
in
ot
he
r
la
ngua
ge
s
.
M
ul
ti
li
ngua
l
-
C
L
I
P
b
r
id
ge
s
th
is
ga
p
by
ut
il
iz
in
g
tr
a
ns
f
e
r
le
a
r
ni
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1
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28]
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T ← C
t
(
X
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La ← [
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Wa ← [
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For
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[
1
,
5
1
2
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Evaluation Warning : The document was created with Spire.PDF for Python.
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3593
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
W
←
G
.
m
a
p
p
i
n
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(
Z
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Wa ← Wa
∪
{
W
}
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(
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end for
3.5.5. E
val
u
at
io
n
m
e
t
r
ic
s
T
o
e
v
a
lu
a
te
th
e
qua
li
ty
a
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r
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a
li
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m
of
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d
im
a
ge
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s
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m
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va
lu
a
ti
on
m
e
tr
ic
s
a
r
e
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e
d
to
c
om
pa
r
e
th
e
m
w
it
h
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e
a
l
im
a
g
e
s
.
T
w
o
m
e
tr
ic
s
u
s
e
d
a
r
e
F
r
é
c
he
t
in
c
e
pt
io
n
di
s
ta
nc
e
(
F
I
D
)
a
nd
le
a
r
ne
d
pe
r
c
e
pt
ua
l
im
a
ge
pa
tc
h
s
im
il
a
r
it
y (
L
P
I
P
S
)
.
‒
F
r
é
c
he
t
i
nc
e
pt
io
n
di
s
ta
n
c
e
:
a
c
om
m
onl
y
u
s
e
d
m
e
tr
ic
to
m
e
a
s
ur
e
th
e
r
e
a
li
s
m
a
nd
di
v
e
r
s
it
y
of
s
ynt
he
s
iz
e
d
pi
c
tu
r
e
s
is
F
I
D
[
29
]
.
I
n
c
ont
r
a
s
t
to
th
e
in
c
e
pt
io
n
s
c
or
e
,
w
hi
c
h
s
ol
e
ly
f
oc
us
e
s
on
th
e
ge
ne
r
a
te
d
im
a
ge
s
,
F
I
D
a
na
ly
s
e
s
th
e
di
s
tr
ib
ut
io
n
o
f
a
ut
he
nt
ic
a
nd
s
ynt
he
s
iz
e
d
i
m
a
ge
s
e
ts
.
W
he
n
us
in
g
a
n
I
nc
e
pt
io
nV
3
m
ode
l,
th
e
f
e
a
tu
r
e
s
of
th
e
r
e
a
l
a
nd
f
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ke
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e
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a
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E
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c
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ha
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to
a
num
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ic
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dge
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a
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li
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F
r
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c
he
t
di
s
ta
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c
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be
twe
e
n
th
e
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be
ddi
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put
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d
to
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a
s
ur
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th
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s
im
il
a
r
it
y
of
di
s
tr
ib
ut
io
ns
. H
ig
he
r
i
m
a
ge
qua
li
ty
a
nd r
e
a
li
s
ti
c
l
ooks
a
r
e
m
a
r
ke
d by lowe
r
F
I
D
s
c
or
e
s
.
‒
L
e
a
r
ne
d
pe
r
c
e
pt
ua
l
im
a
ge
pa
tc
h
s
im
il
a
r
it
y
:
p
e
r
c
e
pt
ua
l
lo
s
s
is
a
m
e
tr
ic
th
a
t
f
in
ds
how
s
tr
uc
tu
r
a
ll
y
a
li
ke
two
hi
gh
-
di
m
e
ns
io
na
l
im
a
ge
s
a
r
e
.
I
t
us
e
s
a
d
e
e
p
c
onvolut
i
ona
l
ne
ur
a
l
ne
twor
k
to
obt
a
in
in
tr
ic
a
te
c
ha
r
a
c
te
r
is
ti
c
s
of
im
a
g
e
s
a
nd
de
te
r
m
in
e
how
ne
a
r
th
e
im
a
ge
pa
tc
he
s
'
a
c
ti
va
ti
ons
a
r
e
to
e
a
c
h
ot
h
e
r
.
S
e
ve
r
a
l
la
ye
r
s
in
th
e
s
e
de
e
p
C
N
N
s
e
f
f
e
c
ti
ve
ly
c
a
pt
ur
e
a
b
s
tr
a
c
t
vi
s
ua
l
r
e
pr
e
s
e
nt
a
ti
ons
.
L
P
I
P
S
[
30]
is
us
e
f
ul
in
c
om
pa
r
in
g
th
e
r
e
s
e
m
bl
a
nc
e
of
a
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a
l
im
a
ge
a
nd
it
s
c
or
r
e
s
ponding
ge
ne
r
a
te
d
im
a
ge
.
L
P
I
P
S
is
known to be
c
om
pa
r
a
bl
e
t
o huma
n pe
r
c
e
pt
io
n.
3.6.
E
xp
e
r
im
e
n
t
in
g w
it
h
C
L
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P
an
d
i
t
s
var
ia
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t
s
T
hi
s
s
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im
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d
to
in
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s
ti
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a
bi
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s
of
C
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a
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it
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s
in
va
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s
a
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da
ta
s
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ts
.
C
L
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P
,
M
e
ta
C
L
I
P
[
17]
,
a
nd
m
ul
ti
li
ngua
l
-
C
L
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P
w
e
r
e
t
e
s
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on
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of
2,100
c
a
pt
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f
r
om
th
e
C
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le
bA
da
ta
s
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t.
D
ue
to
th
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C
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m
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l'
s
c
ont
e
xt
le
ngt
h
li
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it
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on,
onl
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th
e
two
lo
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t
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nt
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nc
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s
in
e
a
c
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c
a
pt
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a
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us
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d
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or
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lu
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ti
on.
A
n
a
ly
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is
of
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to
m
S
ty
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m
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in
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d
on
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of
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im
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f
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m
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w
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not
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us
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d a
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a
r
ni
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a
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of
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ps
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or
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w
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tr
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hs
of
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C
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va
r
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s
,
th
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V
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-
B
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s
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m
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or
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th
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on
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M
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C
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P
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th
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V
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-
B
-
32
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qui
c
kg
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m
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d
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1
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in
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ngua
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th
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s
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r
w
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s
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it
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r
ta
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32
te
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r
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m
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r
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a
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pr
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li
ngua
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oc
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s
s
in
g
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s
k
s
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pr
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tr
a
in
e
d
in
109 la
ngua
ge
s
, w
hi
c
h pr
oduc
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s
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m
be
ddi
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s
of
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ki
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t
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it
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a
na
ly
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v
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, M
e
t
a
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a
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li
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im
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c
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M
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L
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G
A
N
2
F
F
H
Q
m
ode
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M
ul
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ngua
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20
25
:
3588
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3598
3596
C
L
I
P
f
a
ll
s
be
hi
nd
C
L
I
P
a
nd
M
e
ta
C
L
I
P
in
F
I
D
,
L
P
I
P
S
,
a
nd
C
L
I
P
va
r
ia
nt
lo
s
s
s
c
or
e
s
a
nd
h
a
s
i
s
s
ue
s
r
e
pr
e
s
e
nt
in
g c
e
r
ta
in
a
tt
r
ib
ut
e
s
i
n t
he
t
e
xt
c
a
pt
io
ns
, p
a
r
ti
c
ul
a
r
ly
i
n T
a
m
il
.
F
ig
ur
e
s
2
a
nd
3,
di
s
c
us
s
th
e
lo
s
s
c
ur
ve
s
a
nd
c
onve
r
ge
nc
e
of
th
e
C
L
I
P
va
r
ia
nt
s
a
nd
S
ty
le
G
A
N
m
ode
ls
.
I
n
F
ig
ur
e
2,
M
e
ta
C
L
I
P
’
s
lo
s
s
de
c
r
e
a
s
e
s
qui
c
kl
y
in
it
ia
ll
y,
s
lo
w
in
g
dow
n
a
r
ound
th
e
50t
h
s
te
p.
C
L
I
P
s
ta
r
ts
w
it
h
a
hi
ghe
r
lo
s
s
th
a
n
th
e
ot
h
e
r
C
L
I
P
va
r
ia
nt
s
but
f
ol
lo
w
s
a
s
im
il
a
r
tr
e
nd
to
M
e
ta
C
L
I
P
.
T
he
m
ul
ti
li
ngua
l
-
C
L
I
P
m
ode
l
s
how
s
a
s
te
a
dy
de
c
r
e
a
s
e
in
lo
s
s
va
lu
e
s
,
w
it
h
th
e
E
ngl
is
h
m
ode
l
pe
r
f
or
m
in
g
s
li
ght
ly
be
tt
e
r
th
a
n
th
e
T
a
m
il
one
.
I
n
c
ont
r
a
s
t,
th
e
F
F
H
Q
S
ty
le
G
A
N
2
m
ode
l,
in
F
ig
ur
e
3,
di
s
pl
a
ys
be
tt
e
r
pe
r
f
or
m
a
nc
e
f
or
a
ll
th
e
C
L
I
P
m
ode
ls
th
a
n
th
e
c
us
to
m
F
a
ir
F
a
c
e
S
ty
le
G
A
N
2
-
A
DA
m
ode
l.
M
e
ta
C
L
I
P
a
c
hi
e
ve
s
th
e
be
s
t
pe
r
f
or
m
a
nc
e
w
it
h
a
s
te
e
pe
r
a
nd
lo
w
e
r
lo
s
s
c
ur
ve
,
w
hi
le
C
L
I
P
a
nd
m
ul
ti
li
ngua
l
-
C
L
I
P
ha
ve
s
im
il
a
r
c
our
s
e
s
,
w
it
h a
n i
ni
ti
a
ll
y s
ha
r
p de
c
r
e
a
s
e
i
n t
he
l
o
s
s
f
unc
ti
on t
ha
t
th
e
n gr
a
dua
ll
y s
ta
bi
li
z
e
s
.
4.2. Dis
c
u
s
s
io
n
T
hi
s
pa
pe
r
a
na
ly
s
e
s
th
e
pe
r
f
or
m
a
nc
e
of
di
f
f
e
r
e
nt
S
ty
le
G
A
N
m
ode
ls
[
9]
,
[
11]
a
nd
C
L
I
P
va
r
ia
nt
s
in
a
tr
a
in
in
g
-
f
r
e
e
te
xt
-
to
-
f
a
c
e
ge
n
e
r
a
ti
on
m
ode
l.
E
a
r
li
e
r
r
e
s
e
a
r
c
h
e
xa
m
in
e
d
te
xt
-
to
-
f
a
c
e
ge
ne
r
a
ti
on
w
it
h
C
L
I
P
,
bu
t
th
e
r
e
ha
s
be
e
n
li
tt
le
in
ve
s
ti
ga
ti
on
of
M
e
ta
C
L
I
P
,
a
r
e
c
e
nt
de
v
e
lo
pm
e
nt
tr
a
in
e
d
on
one
bi
ll
io
n
im
a
ge
-
te
xt
pa
ir
s
.
M
ul
ti
li
ngua
l
-
C
L
I
P
ha
s
ha
d
in
a
de
qua
te
r
e
s
e
a
r
c
h
a
nd
c
a
n
a
s
s
is
t
in
m
ul
ti
li
ngua
l
te
xt
-
to
-
f
a
c
e
ge
ne
r
a
ti
on.
T
hi
s
r
e
s
e
a
r
c
h
a
im
s
to
unde
r
s
ta
nd
th
e
a
dva
nt
a
ge
s
a
nd
di
s
a
dv
a
nt
a
g
e
s
of
C
L
I
P
a
nd
it
s
va
r
ia
nt
s
by
c
onduc
ti
ng
a
c
om
pa
r
a
ti
ve
s
tu
dy.
O
ur
in
ve
s
ti
ga
ti
on
f
ound
th
a
t
th
e
S
ty
le
G
A
N
2
m
ode
l
pr
e
-
tr
a
in
e
d
on
th
e
F
F
H
Q
da
ta
s
e
t
s
ur
pa
s
s
e
s
th
e
c
u
s
to
m
S
ty
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G
A
N
2
-
A
DA
m
ode
l
in
r
e
a
li
s
m
,
a
s
in
di
c
a
te
d
by
th
e
lo
w
e
r
F
I
D
s
c
or
e
s
,
how
e
ve
r
,
th
e
S
ty
le
G
A
N
2
-
A
DA
m
ode
ls
pr
oduc
e
d
m
or
e
di
ve
r
s
e
a
nd
f
a
ir
i
m
a
ge
s
.
T
h
e
F
F
H
Q
m
ode
l,
tr
a
in
e
d
on
70,000
im
a
ge
s
,
s
how
e
d
be
tt
e
r
f
e
a
tu
r
e
r
e
pr
e
s
e
nt
a
ti
on
th
a
n
th
e
F
a
ir
F
a
c
e
m
ode
l
[
21]
,
tr
a
in
e
d
on
onl
y
10,500
im
a
ge
s
.
T
he
im
a
ge
s
s
ynt
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s
iz
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d
by
th
e
pr
e
-
tr
a
in
e
d
S
ty
le
G
A
N
2
m
ode
l
a
r
e
of
s
upe
r
io
r
qua
li
ty
a
nd
c
la
r
it
y,
w
hi
c
h
a
id
e
d
C
L
I
P
a
nd
it
s
v
a
r
ia
nt
s
in
a
s
s
oc
ia
ti
ng
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e
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xt
r
a
c
te
d
f
e
a
tu
r
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s
of
th
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s
iz
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d
im
a
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to
th
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ir
t
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xt
de
s
c
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ip
ti
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,
le
a
di
ng
to
be
tt
e
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c
onv
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e
.
M
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C
L
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s
ynt
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iz
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im
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ur
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th
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s
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ip
ti
on,
w
he
r
e
a
s
C
L
I
P
c
r
e
a
te
s
th
e
m
os
t
r
e
a
li
s
ti
c
im
a
ge
s
. M
e
ta
C
L
I
P
'
s
lo
w
L
P
I
P
S
s
c
or
e
c
oul
d
be
due
to
it
s
di
ve
r
s
e
a
nd
e
a
s
il
y
tr
a
ve
r
s
a
bl
e
e
m
be
ddi
ng
s
pa
c
e
.
T
hi
s
is
e
m
ph
a
s
iz
e
d
by
M
e
ta
C
L
I
P
'
s
s
ha
r
p
a
nd
lo
w
c
ur
ve
s
how
n
in
F
ig
ur
e
3.
C
L
I
P
d
is
pl
a
ys
th
e
be
s
t
r
e
a
li
s
m
,
de
not
e
d
by
it
s
F
I
D
s
c
or
e
s
a
nd
be
tt
e
r
c
onve
r
ge
nc
e
th
a
n
th
e
m
ul
ti
li
ngua
l
-
C
L
I
P
m
ode
l.
M
ul
ti
li
ngua
l
-
C
L
I
P
’
s
T
a
m
il
a
nd
E
ng
li
s
h
pe
r
f
or
m
a
nc
e
w
a
s
s
a
ti
s
f
a
c
to
r
y
in
br
id
gi
ng
th
e
ga
p
in
m
ul
ti
li
ngua
l
te
xt
-
to
-
im
a
ge
ge
ne
r
a
ti
on.
F
in
a
ll
y,
te
s
ti
ng
dur
a
ti
ons
f
or
C
L
I
P
a
nd
m
ul
ti
li
ngua
l
-
C
L
I
P
w
e
r
e
s
hor
te
r
t
ha
n M
e
ta
C
L
I
P
due
t
o t
he
l
a
r
ge
a
m
ount
of
i
nf
or
m
a
ti
on t
ha
t
M
e
ta
C
L
I
P
pr
oc
e
s
s
e
s
w
hi
le
t
e
s
ti
ng.
A
c
c
or
di
n
g
t
o
ou
r
ou
tc
om
e
s
,
i
t
c
a
n
be
c
on
c
lu
de
d
t
ha
t
M
e
t
a
C
L
I
P
'
s
m
a
s
s
i
ve
a
m
ou
nt
of
hi
gh
-
q
ua
li
t
y
tr
a
i
ni
n
g
da
ta
c
on
tr
ib
ut
e
s
t
o
th
e
pow
e
r
f
ul
a
nd
e
f
f
ic
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e
nt
na
vi
g
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t
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on
of
it
s
s
ha
r
e
d
e
m
b
e
d
di
n
g
s
p
a
c
e
.
T
hi
s
s
t
ud
y
r
e
in
f
or
c
e
s
M
e
t
a
C
L
I
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'
s
s
u
pe
r
io
r
p
e
r
f
or
m
a
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n
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s
u
a
l
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g
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s
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c
t
a
s
k
s
c
o
m
p
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r
e
d
t
o
C
L
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P
.
M
ul
t
il
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l
-
C
L
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,
w
hi
c
h
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ti
l
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d
R
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B
E
R
T
a
,
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n
d
tr
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in
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m
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s
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of
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a
ta
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C
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m
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. T
h
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T
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m
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m
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a
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C
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m
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s
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s
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due
to
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of
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ta
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tr
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r
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m
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t
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i
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l
m
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O
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a
r
c
h
h
a
d
t
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f
ol
lo
w
in
g
li
m
it
a
ti
on
s
.
T
h
e
r
e
w
e
r
e
h
a
r
d
w
a
r
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n
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c
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it
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t
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t
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t
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tr
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S
t
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G
A
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2
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A
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m
o
de
l
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n
c
r
e
a
s
in
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t
h
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e
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h
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F
a
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F
a
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t
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pr
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a
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ta
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d e
nr
i
c
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th
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s
m
of
t
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m
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l.
T
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s
t
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pr
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c
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a
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n
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e
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n
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xt
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to
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s
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s
.
R
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s
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v
a
ti
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ta
bl
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s
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d
M
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t
a
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s
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it
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e
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e
r
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C
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S
[
1]
I
.
J
.
G
oo
d
f
e
l
l
ow
e
t
a
l
.
, “
G
e
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
s
,”
i
n
A
d
v
an
c
e
s
i
n
ne
ur
al
i
nf
or
m
at
i
o
n pr
oc
e
s
s
i
ng
s
y
s
t
e
m
s
,
20
14,
p
p.
26
72
–
26
80
.
[
2]
J
.
S
ohl
-
D
i
c
ks
t
e
i
n,
E
.
A
.
W
e
i
s
s
,
N
.
M
a
he
s
w
a
r
a
na
t
ha
n,
a
nd
S
.
G
a
ngul
i
,
“
D
e
e
p
uns
upe
r
vi
s
e
d
l
e
a
r
ni
ng
u
s
i
ng
none
qui
l
i
br
i
um
t
he
r
m
odyna
m
i
c
s
,”
32nd I
nt
e
r
nat
i
onal
C
onf
e
r
e
n
c
e
on M
ac
hi
ne
L
e
ar
ni
ng, I
C
M
L
2015
, vol
. 3, pp. 2246
–
2255, 2015.
[
3]
M
. M
i
r
z
a
a
nd S
. O
s
i
nd
e
r
o, “
C
ondi
t
i
ona
l
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
s
,”
ar
X
i
v
-
C
o
m
put
e
r
Sc
i
e
nc
e
,
pp. 1
-
7, N
ov.
2014
.
[
4]
A
.
R
a
df
or
d,
L
.
M
e
t
z
,
a
nd
S
.
C
hi
nt
a
l
a
,
“
U
ns
upe
r
vi
s
e
d
r
e
pr
e
s
e
nt
a
t
i
on
l
e
a
r
ni
n
g
w
i
t
h
de
e
p
c
onvol
ut
i
ona
l
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
w
or
ks
,”
4t
h I
nt
e
r
nat
i
onal
C
onf
e
r
e
n
c
e
on L
e
ar
ni
ng R
e
pr
e
s
e
nt
at
i
ons
-
C
onf
e
r
e
nc
e
T
r
ac
k
P
r
oc
e
e
di
ngs
, 2015
, pp. 1
-
16
.
[
5]
T
.
X
u
e
t
al
.
,
“
A
t
t
nG
A
N
:
f
i
ne
-
g
r
a
i
ne
d
t
e
xt
t
o
i
m
a
ge
ge
ne
r
a
t
i
on
w
i
t
h
a
t
t
e
nt
i
ona
l
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
w
or
ks
,”
2018
I
E
E
E
/
C
V
F
C
onf
e
r
e
nc
e
on C
om
put
e
r
V
i
s
i
on and P
at
t
e
r
n R
e
c
ogni
t
i
on
, pp. 1316
–
1324, 2018, doi
:
10.1109/
C
V
P
R
.2018.00143.
[
6]
H
.
Z
ha
ng
e
t
al
.
,
“
S
t
a
c
kG
A
N
:
t
e
xt
t
o
phot
o
-
r
e
a
l
i
s
t
i
c
i
m
a
ge
s
ynt
h
e
s
i
s
w
i
t
h
s
t
a
c
ke
d
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
w
or
ks
,”
P
r
oc
e
e
di
ng
s
of
t
he
I
E
E
E
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on C
om
put
e
r
V
i
s
i
on
, pp. 5908
–
5916, 2017, doi
:
10.1109/
I
C
C
V
.2017.629.
[
7]
T
. K
a
r
r
a
s
, S
. L
a
i
ne
, a
nd T
. A
i
l
a
, “
A
s
t
yl
e
-
ba
s
e
d ge
ne
r
a
t
or
a
r
c
hi
t
e
c
t
ur
e
f
or
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
w
or
ks
,”
I
E
E
E
T
r
ans
ac
t
i
ons
on
P
at
t
e
r
n A
nal
y
s
i
s
and M
ac
hi
ne
I
nt
e
l
l
i
ge
nc
e
, vol
. 43, no. 12, pp. 4217
–
4228, 2021, doi
:
10.1109/
T
P
A
M
I
.2020.2970919.
[
8]
T
.
K
a
r
r
a
s
,
T
.
A
i
l
a
,
S
.
L
a
i
ne
,
a
nd
J
.
L
e
ht
i
ne
n,
“
P
r
ogr
e
s
s
i
ve
gr
ow
i
ng
of
G
A
N
s
f
or
i
m
pr
ove
d
qua
l
i
t
y,
s
t
a
bi
l
i
t
y,
a
nd
va
r
i
a
t
i
on,”
6t
h
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on L
e
a
r
ni
ng R
e
pr
e
s
e
nt
at
i
ons
, I
C
L
R
2018
-
C
onf
e
r
e
nc
e
T
r
ac
k
P
r
oc
e
e
di
ng
s
, 2018
, pp. 1
-
26
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[
9]
T
.
K
a
r
r
a
s
,
S
.
L
a
i
n
e
,
M
.
A
i
t
t
a
l
a
,
J
.
H
e
l
l
s
t
e
n
,
J
.
L
e
h
t
i
n
e
n
,
a
n
d
T
.
A
i
l
a
,
“
A
n
a
l
y
z
i
ng
a
n
d
i
m
p
r
ov
i
n
g
t
h
e
i
m
a
ge
q
ua
l
i
t
y
o
f
s
t
y
l
e
ga
n
,
”
2
0
20
I
E
E
E
/
C
V
F
C
o
n
f
e
r
e
nc
e
on
C
om
p
u
t
e
r
V
i
s
i
on
a
n
d
P
a
t
t
e
r
n
R
e
c
o
g
n
i
t
i
o
n
,
p
p
.
81
0
7
–
81
16
,
2
0
2
0,
d
o
i
:
1
0
.
11
0
9
/
C
V
P
R
4
2
6
0
0.
2
0
2
0.
0
0
8
13
.
[
10]
T
.
K
a
r
r
a
s
e
t
al
.
,
“
A
l
i
a
s
-
f
r
e
e
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
w
or
ks
,”
A
dv
anc
e
s
i
n
N
e
ur
al
I
nf
or
m
at
i
on
P
r
oc
e
s
s
i
ng
S
y
s
t
e
m
s
,
vol
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pp. 852
–
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T
. K
a
r
r
a
s
, M
.
A
i
t
t
a
l
a
, J
. H
e
l
l
s
t
e
n, S
.
L
a
i
ne
,
J
. L
e
ht
i
ne
n, a
nd
T
. A
i
l
a
, “
T
r
a
i
ni
ng
ge
ne
r
a
t
i
ve
a
dve
r
s
a
r
i
a
l
ne
t
w
or
ks
w
i
t
h
l
i
m
i
t
e
d da
t
a
,
”
A
dv
anc
e
s
i
n N
e
u
r
al
I
nf
or
m
at
i
on P
r
oc
e
s
s
i
ng Sy
s
t
e
m
s
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[
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D
.
M
.
A
.
A
ya
nt
hi
a
nd
S
.
M
una
s
i
nghe
,
“
T
e
xt
-
to
-
f
a
c
e
ge
ne
r
a
t
i
on
w
i
t
h
s
t
yl
e
G
A
N
2,”
C
om
put
e
r
Sc
i
e
nc
e
&
I
nf
or
m
at
i
on
T
e
c
hnol
og
y
,
pp. 49
–
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:
10.5121/
c
s
i
t
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[
13]
M
.
S
.
S
a
ba
e
,
M
.
A
.
D
a
r
di
r
,
R
.
T
.
E
s
ka
r
ous
,
a
nd
M
.
R
.
E
bbe
d,
“
S
t
yl
e
T
2F
:
ge
n
e
r
a
t
i
ng
hum
a
n
f
a
c
e
s
f
r
om
t
e
xt
ua
l
de
s
c
r
i
pt
i
on
us
i
ng
s
t
yl
e
G
A
N
2,”
ar
X
i
v
-
C
om
put
e
r
Sc
i
e
nc
e
, pp. 1
-
10,
A
pr
.
2022
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[
14]
A
.
R
a
df
or
d
e
t
al
.
,
“
L
e
a
r
ni
ng
t
r
a
ns
f
e
r
a
bl
e
vi
s
ua
l
m
ode
l
s
f
r
om
na
t
ur
a
l
l
a
ngua
g
e
s
upe
r
vi
s
i
on,”
P
r
oc
e
e
di
ngs
of
M
ac
hi
ne
L
e
a
r
ni
ng
R
e
s
e
ar
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h
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O
.
P
a
t
a
s
h
ni
k
,
Z
.
W
u,
E
.
S
he
c
h
t
m
a
n,
D
.
C
ohe
n
-
O
r
,
a
nd
D
.
L
i
s
c
h
i
ns
k
i
,
“
S
t
yl
e
C
L
I
P
:
t
e
xt
-
d
r
i
ve
n
m
a
n
i
p
ul
a
t
i
on
o
f
S
t
yl
e
G
A
N
i
m
a
ge
r
y,
”
202
1
I
E
E
E
/
C
V
F
I
n
t
e
r
n
at
i
on
al
C
o
nf
e
r
e
nc
e
on
C
o
m
pu
t
e
r
V
i
s
i
on
(
I
C
C
V
)
,
pp
. 2
06
5
–
20
74,
2
021
,
do
i
:
10
.11
09
/
I
C
C
V
4
892
2.
202
1.
002
09
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[
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W
.
X
i
a
,
Y
.
Y
a
ng,
J
.
H
.
X
ue
,
a
nd
B
.
W
u,
“
T
e
di
G
A
N
:
t
e
xt
-
gui
de
d
di
ve
r
s
e
f
a
c
e
i
m
a
ge
ge
ne
r
a
t
i
on
a
nd
m
a
ni
pul
a
t
i
on,”
202
1
I
E
E
E
/
C
V
F
C
onf
e
r
e
nc
e
on
C
om
put
e
r
V
i
s
i
on
and
P
at
t
e
r
n
R
e
c
ogni
t
i
on
(
C
V
P
R
)
,
pp.
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C
V
P
R
46437.2021.00229.
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