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Addressing GAN Training Instabilities via Tunable Classification Losses
Generalizing this dual-objective formulation using CPE losses, we define and obtain upper bounds on an appropriately defined estimation error.
$(α_D,α_G)$-GANs: Addressing GAN Training Instabilities via Dual Objectives
In an effort to address the training instabilities of GANs, we introduce a class of dual-objective GANs with different value functions (objectives) for the generator (G) and discriminator (D).
AugLoss: A Robust Augmentation-based Fine Tuning Methodology
As a step towards addressing both problems simultaneously, we introduce AugLoss, a simple but effective methodology that achieves robustness against both train-time noisy labeling and test-time feature distribution shifts by unifying data augmentation and robust loss functions.
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