MAXIMUM ENTROPY GENERATIVE ADVERSARIAL NETWORK BASED ON ENERGY MODELS

Generative adversarial network (GAN) is a hot topic in the field of deep learning. Aiming at the problem of GAN model such as mode collapse and training instability in the training process, we propose a new energy maximum entropy GAN (E-MEGAN). The ultimate goal of the model was to maximize the entropy of the generated samples to solve the problem of mode collapse, and we used the non-parametric mutual information estimator to calculate the entropy. At the same time, in order to stabilize the adversarial training process, we also used a zero-centered gradient penalty technique. Through a large number of experiments on the MNIST and CelebA datasets, it is shown that this model can generate clear and high-quality images, and its inception scores (IS) and Fréchet inception distance (FID) are equally competitive compared with WGAN-GP technology. The method also will not suffer from mode collapse.