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Adversarial Fisher Vectors for Unsupervised Representation Learning

NeurIPS 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Contrastive representation theory · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use AFVs instead of pooled discriminator features (ablation)GAN discriminator features · CIFAR10classification accuracy: 65.3 -> 86.2Helped
Add data augmentation during GAN training (ablation)AFV-128-50000 · CIFAR10classification accuracy: 86.2 -> 87.1Helped
Increase model channel size to 256 (ablation)AFV-128-50000 + augment · CIFAR10classification accuracy: 87.1 -> 88.5Helped
Add CIFAR100 training data (ablation)AFV-256-50000 + augment · CIFAR10classification accuracy: 88.5 -> 89.1Helped
Add CIFAR100 training data (ablation)AFV-256-50000 + augment · CIFAR100classification accuracyHelped
Use AFV as the perceptual similarity metricImageNet-trained GAN · BAPPS traditional distortions2AFC: 70.6 -> 74.9Helped
Use AFV as the perceptual similarity metricImageNet-trained GAN · BAPPS CNN distortions2AFC: 83.1 -> 80.2Hurt
Use AFV as the perceptual similarity metricImageNet-trained GAN · BAPPS averageAvg 2AFC: 78.0 -> 77.6Hurt
Use AFV as the perceptual similarity metricImageNet-trained GAN · BAPPS traditional distortionsJND: 44.0 -> 55.7Helped
Use AFV as the perceptual similarity metricImageNet-trained GAN · BAPPS CNN distortionsJND: 67.6 -> 62.0Hurt
Use AFV as the perceptual similarity metricImageNet-trained GAN · BAPPS averageAvg JND: 55.6 -> 58.9Helped
Constrain generator updates to be local with the MCMC-inspired objective (ablation)GAN generator · ImageNet 64x64Inception ScoreHelped

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