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TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

NeurIPS 2021 · Architectures and efficiency › Attention and transformers · approach: Efficient and robust vision transformers · 263 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use TransGAN instead of StyleGAN-V2 + DiffAug.CIFAR-10 image generation · CIFAR-10FID: 9.89 -> 9.26Helped
Use TransGAN instead of StyleGAN-V2 + DiffAug.STL-10 image generation · STL-10IS: 10.31* -> 10.43Helped
Use TransGAN instead of StyleGAN-V2 + DiffAug.STL-10 image generation · STL-10FID: 19.15* -> 18.28Helped
Use TransGAN instead of StyleGAN-V2 + DiffAug.CelebA 128x128 image generation · CelebA 128x128FID: 5.40* -> 5.28Helped
Add DiffAug (ablation)TransGAN · CIFAR-10FID: 22.53 -> 9.26Helped
Add DiffAug (ablation)TransGAN · CIFAR-10IS: 8.36 -> 9.02Helped
Add DiffAug (ablation)WGAN-GP · CIFAR-10IS: 6.49 -> 6.29Hurt
Add DiffAug (ablation)AutoGAN · CIFAR-10FID: 12.42 -> 12.72Hurt
Replace grid self-attention with Nyström or axis self-attention (ablation)TransGAN · CelebA 64x64, CelebA 128x128, and LSUN Church 256x256FID: Grid: 9.89, 10.58, 20.39; Nyström: 13.47, 17.42, 39.92; Axis: 12.39, 13.95, 29.30Hurt
Replace standard self-attention with grid self-attention (ablation)TransGAN · CelebA 128x128 and LSUN Church 256x256FID: OOM -> 10.58; OOM -> 20.39Helped
Add a multi-scale discriminator (ablation)TransGAN with grid self-attention · CelebA 64x64, CelebA 128x128, and LSUN Church 256x256FID: 9.89 -> 9.28; 10.58 -> 8.03; 20.39 -> 15.29Helped
Add modified normalization (ablation)TransGAN with multi-scale discriminator · CelebA 64x64, CelebA 128x128, and LSUN Church 256x256FID: 9.28 -> 7.05; 8.03 -> 7.13; 15.29 -> 13.27Helped

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