Antimode collapse generative adversarial networks

Abstract

Generative adversarial networks (GANs) are a class of techniques widely applied in image synthesis, recovery, compensation, and other related fields. We propose and introduce a novel, improved conditional model antimode collapse GANs (AMCGANs). Through a newly designed network architecture and optimizing strategy, the function of the class label information is moderately constrained and it no longer directly influences the discriminator, and hence the synthesized images belonging to the same classes will not be excessively concentrated due to the attraction of the same labels. Thus, the mode collapse problem, namely generating homogeneity, which always hinders image synthesization approaches can be effectively restrained. On the other hand, by sharing the feature extraction part and only updating its weights during the training of discriminator, AMCGANs achieves relatively efficient computing performance. In addition, it works well both for supervised and for semisupervised learning circumstances. Extensive experiments have been conducted on the Fashion-MNIST and CIFAR10 data sets to verify the effectiveness of the proposed approach.