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Training GANs with Stronger Augmentations via Contrastive Discriminator

ICLR 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Data augmentation and generalization bias · 30 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use ContraD with SimCLR augmentations instead of bCR with HFlip and translationSNDCGAN · CIFAR-10FID: 14.0 -> 10.9Helped
Use ContraD with SimCLR augmentations instead of bCR with HFlip and translationSNDCGAN · CIFAR-100FID: 19.2 -> 15.2Helped
Use ContraD with SimCLR augmentations instead of bCRSNResNet-18 discriminator GAN · CIFAR-10FID: 22.8 -> 9.86Helped
Use ContraD with SimCLR augmentations instead of bCRSNResNet-18 discriminator GAN · CIFAR-100FID: 28.2 -> 15.0Helped
Add ContraD instead of bCRSNDCGAN · CelebA-HQ-128FID: 19.5 -> 17.1Helped
Use ContraD instead of ADAStyleGAN2 · AFHQ-CatFID: 3.55 -> 3.82Hurt
Use ContraD instead of ADAStyleGAN2 · AFHQ-Dog and AFHQ-WildFID: AFHQ-Dog: 7.40 -> 7.16; AFHQ-Wild: 3.05 -> 2.54Helped
Add stop-gradient before the discriminator head (ablation)ContraD · CIFAR-10FID: SNDCGAN: 11.6 -> 11.1; SNResNet-18: 28.0 -> 10.6Helped
Use a 2-layer MLP instead of a linear discriminator head (ablation)ContraD · CIFAR-10FID: SNDCGAN: 185 -> 11.1; SNResNet-18: 274 -> 10.6Helped
Add the real-sample contrastive loss L⁺con (ablation)ContraD · CIFAR-10FID: SNDCGAN: 11.9 -> 11.1; SNResNet-18: 182 -> 10.6Helped
Add the fake-sample contrastive loss L⁻con (ablation)ContraD · CIFAR-10FID: SNDCGAN: 210 -> 11.1; SNResNet-18: 232 -> 10.6Helped
Replace SimCLR augmentations with HFlip and Trans (ablation)ContraD · CIFAR-10FID: 10.9 -> 13.7Hurt

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