cailk / AlphaGAN

Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks

Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks

By Likun Cai, Yanjie Chen, Ning Cai, Wei Cheng, Hao Wang.

This repo is the official implementation of AlphaGAN.

Introduction

We propose the Alpha-divergence Generative Adversarial Net (Alpha-GAN). It adopts the alpha divergence as the minimization objective function of generators. The alpha divergence can be regarded as a generalization of the Kullback–Leibler divergence, Pearson divergence, Hellinger divergence, etc. Our Alpha-GAN employs the power function as the form of adversarial loss for the discriminator with two order indices, and these hyper-parameters make our model more flexible to balance between the generated and target distributions.