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

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 4; p. 410
• Main Authors: Cai, Likun, Chen, Yanjie, Cai, Ning, Cheng, Wei, Wang, Hao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 04.04.2020; MDPI
• Subjects: Alpha divergence; Chi-square test; deep neural networks; generative adversarial network; Generative adversarial networks; Generators; Image processing; Image quality; Image resolution; Machine learning; Optimization; Order parameters; Quality; Stability analysis; Training; unsupervised image generation

Generative Adversarial Nets (GANs) are one of the most popular architectures for image generation, which has achieved significant progress in generating high-resolution, diverse image samples. The normal GANs are supposed to minimize the Kullback–Leibler divergence between distributions of natural a...