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cs.LG2023
Addressing GAN Training Instabilities via Tunable Classification Losses
Monica Welfert, Gowtham R. Kurri, Kyle Otstot +1
Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting t…
cs.LG2023
-GANs: Addressing GAN Training Instabilities via Dual Objectives
Monica Welfert, Kyle Otstot, Gowtham R. Kurri +1
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 discri…