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Found 3 papers, 2 papers with code
On Data Augmentation for GAN Training
We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the Jensen-Shannon (JS) divergence between the original distribution and model distribution.
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game
From the analysis, we identify issues of SS tasks which allow a severely mode-collapsed generator to excel the SS tasks.
Ranked #4 on
Image Generation
on ImageNet 32x32
An Improved Self-supervised GAN via Adversarial Training
Importantly, we find out that simultaneously training the discriminator to classify the fake class from the pseudo-classes of real samples for the classification task will improve the discriminator and subsequently lead better guides to train generator.
