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MethodFID↓KID↓
ABCAllABCAll
Mokady (2020)68.6966.9036.2158.630.0500.0360.0300.036
StarGAN v21137.70-110.013
StyleGAN v290.1052.95138.0935.340.0720.0270.1860.013
BigGAN + DiffAug218.74134.41270.89155.880.2200.1210.3780.099
Ours58.4336.4422.6829.730.0250.0130.0120.009
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MethodResNet-50EfficientNet-b4
No-Aug21.64±1.2412.06±0.64
Trad-Aug12.58±0.819.33±0.73
Mokady (2020)11.11±1.1913.26±1.13
StarGAN v213.07±1.3012.25±0.79
StyleGAN v211.55±1.7911.68±0.76
BigGAN+DiffAug11.45±0.6112.06±0.50
Ours9.9±0.699.14±1.02
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Trad-AugvAVBvCvABC
A13.81±2.3611.81±2.6512.72±2.8711.99±1.6311.09±3.49
B6.80±1.646.40±1.346.60±1.526.59±1.345.60±1.34
C16.57±3.2013.14±2.8111.23±0.8014.85±1.7311.42±0.96
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Dataset Size20AAll
Trad-AugOursTrad-AugOurs
450015.55±0.6314.28±1.2512.75±0.6111.04±0.76
660016.69±0.7614.41±3.1213.07±1.5710.60±0.48
1200016.95±1.0214.22±1.5312.05±0.819.90±0.69
1860016.12±2.1915.36±0.8612.37±0.3210.21±0.96
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Dataset SizeAll
Trad-AugLatentReferenceMix
450012.75±0.6110.72±0.9611.48±0.8811.04±0.76
660013.07±1.5710.34±1.8611.55±1.6410.60±0.48
1200012.05±0.819.90±1.2610.40±0.999.90±0.69
1860012.37±0.3211.04±1.2612.12±0.7510.21±0.96
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(a)Encoder
LayerResampleNormOutput Shape
Image x-128 × 128×3
Conv 1×1-128 ×128 ×128
ResBlkAvgPoolIN64× 64×256
ResBlkAvgPoolIN32 × 32 × 512
ResBlkAvgPoolIN16 × 16 × 512
ResBlkIN16 ×16× 512
ResBlkIN16 ×16 × 512
(b) Background Decoder(c) Foreground Decoder
LayerResampleNorm Output ShapeLayerResample NormOutput Shape
Input-16 × 16 × 448Input ResBlk16 × 16 × 64
ResBlkIN 16 ×16× 448 16 ×16× 512ResBlkAdaIN16 ×16× 64
ResBlkIN=AdaIN16 × 16 × 256
ResBlk=IN16 ×16 × 512ResBlkAdaIN16 ×16× 256
ResBlkUpsampleIN32 × 32×512ResBlk Upsample ResBlkAdaIN32 × 32 × 256
ResBlk ResBlkUpsample UpsampleIN IN64 × 64× 256 128 × 128× 448UpsampleAdaIN64×64×128
ResBlkUpsampleAdaIN128 ×128 × 64
(d) Fusion
LayerResampleNormOutput Shape
Input128 × 128 × (448 + 64)
Conv 1×1128 × 128× 3
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LayerActivationOutput Shape
Latent z=16
LinearReLU512
LinearReLU512
LinearReLU512
LinearReLU512
(b) Style Code(c) Content
LayerActivation Output ShapeLayerResample ActivationNoise Output Shape
Input=512Input=512
LinearReLU512Reshape--1×1×512
LinearReLU512ResBlkUpsampleINTrue2×2×512
LinearReLU512ResBlkUpsampleINTrue4×4×512
Linear164ResBlkUpsampleINTrue8×8×256
ResBlkUpsampleINTrue16 ×16×128
Conv 1×1=INTrue16 × 16× 64
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(a)SharedLayers
LayerResampleNormOutput Shape
Input x128 × 128 ×3
Conv 1×11128 × 128 × 64
ResBlkAvgPool64 × 64× 256
ResBlkAvgPool32 × 32×512
ResBlkAvgPool16 ×16 × 512
(b) Style Code /Discriminator and BG Classifier(c) Content /FG Classifier
LayerResample NormOutput ShapeLayerResample NormOutput Shape
Input-16 ×16× 512Input16 ×16× 512
ResBlkAvgPool8×8×512LReLU16 ×16 × 512
ResBlkAvgPool4×4×512Conv 1×1*K16×16×64*K
LReLU4×4×512
Conv 4×41×1×512
LReLU1×1×512
Reshape512
Linear *KD*K
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Dataset SizeA
Trad-AugvAvBvCvABC
450016.00±1.0412.90±2.6113.08±1.6514.90±2.4615.27±3.49
660014.90±1.3813.99±1.8911.26±1.0414.36±4.0416.00±2.85
1200013.81±2.3611.81±2.6512.72±2.8711.99±1.6311.09±3.49
1860013.63±2.2212.72±5.2214.36±3.8314.18±5.0513.81±8.56
DatasetB
SizeTrad-AugvAvBvCVABC
45008.79±0.457.80±2.155.60±1.1410.19±0.846.79±1.30
66007.60±1.516.80±1.657.80±1.108.00±2.346.00±1.41
120006.80±1.646.40±1.346.60±1.526.59±1.345.60±1.34
186006.80±1.796.19±1.784.40±1.146.60±1.955.99±1.58
DatasetC
SizeTrad-AugvAVBvCvABC
450017.14±4.6214.85±0.5216.76±2.5813.90±1.9812.00±1.59
660015.23±2.3313.14±1.2413.90±2.2914.28±1.3412.57±1.57
1200016.57±3.2013.14±2.8111.23±0.8014.85±1.7311.42±0.96
1860015.62±0.8513.71±1.7315.99±6.7512.57±3.2612.95±2.98
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Dataset Size20A
ABCABC
Trad-AugOursTrad-AugOursTrad-AugOursTrad-AugOurs
450035.09±2.62 27.64±3.127.8±1.485.6±1.6715.24±1.90 13.14±1.7015.55±0.63 14.28±1.25
660039.64±2.28 27.64±1.658.8±1.646.2±1.6415.81±1.73112.38±1.6516.69±0.76 14.41±3.12
1200034.18±4.39 28.55±7.325.8±0.455.6±1.1416.19±1.17 10.86±1.28 16.95±1.02 14.22±1.53
1860039.45±7.06 32.55±5.047.2±0.845.2±1.1014.86±0.85 13.14±2.06 16.12±2.19 15.36±0.86
Dataset SizeAll
ABCABC
Trad-AugOursTrad-AugOursTrad-AugOursTrad-AugOurs
450016.00±1.04110.18±1.75 8.79±0.455.60±1.5117.13±6.62 14.09±2.2712.75±0.61 11.04±0.76
660014.90±1.3810.54±1.22 7.60±1.516.80±3.1115.23±2.33 11.42±013.07±1.57 10.60±0.48
1200013.81±2.366.72±1.65 6.80±1.644.60±016.57±3.20 13.90±2.5712.05±0.819.90±0.69
1860013.63±2.22 10.54±2.45 6.80±1.794.99±1.87 15.62±0.85 11.61±1.24 12.37±0.32 10.21±0.96
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FID↓KID↓
LatentReferenceMixLatentReferenceMix
(a) Baseline StarGAN v237.7337.9937.700.0130.0130.013
(b)+ Style-Content branches43.9032.6133.360.0170.0110.011
(c)+Foreground classifier37.1432.3427.690.0140.0110.008
(d) + Background classifier34.1232.5030.230.0110.0110.010
(e)+ Separately decoding foreground and background in G48.5238.1134.790.0170.0150.011
(f) + Anchor foreground domain (e.g. No rmal)43.6637.4532.150.0190.0150.011
33.050.0090.011
(g)+ Noise injection in Mapping Network34.4229.730.009
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MethodFID↓KID↓
ABCAllABCAll
Mokady et al. (2020)68.6966.9036.2158.630.0500.0360.0300.036
StarGAN v2--137.701-10.013
StyleGAN v290.1052.95138.0935.340.0720.0270.1860.013
StyleGAN v2 + ADA149.6642.75135.6976.160.1380.0190.1910.055
BigGAN235.66192.89193.61151.430.2480.1990.2760.115
BigGAN + DiffAug218.74134.41270.89155.880.2200.1210.3780.099
Ours58.4336.4422.6829.730.0250.0130.0120.009
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MethodFID↓KID↓
CarpetLeatherTileWoodCarpetLeatherTileWood
Mokady (2020)41.8760.26275.1281.710.040.030.290.04
StarGAN v211=1
StyleGAN v251.3751.60225.96140.010.050.030.230.12
BigGAN + DiffAug34.47101.70391.54113.320.030.070.420.07
Ours22.7986.13321.3575.830.010.070.360.03
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Sum170
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