BOLT-GAN: Bayes-Error-Motivated Objective for Stable GAN Training

We introduce BOLT-GAN, a novel framework for stable GAN training using the Bayes optimal learning threshold (BOLT). The discriminator is trained via the BOLT loss under a standard 1-Lipschitz constraint. This guides the generator to maximize the Bayes error of the discrimination task. We show that the training objective in this case represents a class of metrics on probability measures controlled by a 1-Lipschitz discriminator minimizing an integral probability metric that is upper-bounded by Wasserstein-1 distance. Across four standard image-generation benchmarks, BOLT-GAN improves FID and precision/recall over benchmark GAN frameworks under identical architectures and training budgets. Our experimental findings further confirm the advantage of linking the GAN training objective to a min-max Bayes error criterion.