Annealed Generative Adversarial Networks

Understand

We introduce a novel framework for adversarial training where the target distribution is annealed between the uniform distribution and the data distribution.

• We posited a conjecture that learning under continuous annealing in the nonparametric regime is stable irrespective of the divergence measures in the objective function and proposed an algorithm, dubbed {\ss}-GAN, in corollary.
• In this framework, the fact that the initial support of the generative network is the whole ambient space combined with annealing are key to balancing the minimax game.
• In our experiments on synthetic data, MNIST, and CelebA, {\ss}-GAN with a fixed annealing schedule was stable and did not suffer from mode collapse.