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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).
$α$-GAN: Convergence and Estimation Guarantees
We prove a two-way correspondence between the min-max optimization of general CPE loss function GANs and the minimization of associated $f$-divergences.
Realizing GANs via a Tunable Loss Function
We introduce a tunable GAN, called $\alpha$-GAN, parameterized by $\alpha \in (0,\infty]$, which interpolates between various $f$-GANs and Integral Probability Metric based GANs (under constrained discriminator set).
