Restricting Greed in Training of Generative Adversarial Network
This addresses training stability and diversity problems in GANs for researchers and practitioners, though it appears incremental as it builds on existing GAN models.
The paper tackles the instability and mode collapse issues in Generative Adversarial Networks (GANs) by proposing a novel training strategy to restrict greed, resulting in more stable training and generated samples covering more modes of real data.
Generative adversarial network (GAN) has gotten wide re-search interest in the field of deep learning. Variations of GAN have achieved competitive results on specific tasks. However, the stability of training and diversity of generated instances are still worth studying further. Training of GAN can be thought of as a greedy procedure, in which the generative net tries to make the locally optimal choice (minimizing loss function of discriminator) in each iteration. Unfortunately, this often makes generated data resemble only a few modes of real data and rotate between modes. To alleviate these problems, we propose a novel training strategy to restrict greed in training of GAN. With help of our method, the generated samples can cover more instance modes with more stable training process. Evaluating our method on several representative datasets, we demonstrate superiority of improved training strategy on typical GAN models with different distance metrics.