CVPR 2023 - Improving GAN Training via Feature Space Shrinkage

In this episode we discuss Improving GAN Training via Feature Space Shrinkage by Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng. The paper proposes a new method, called AdaptiveMix, for training Generative Adversarial Networks (GANs) from a robust image classification perspective. The proposed method shrinks data regions in the image representation space of the discriminator, making it easier to train the GANs. Hard samples are constructed by mixing a pair of training images to narrow down the feature distance between hard and easy samples. The proposed approach is evaluated on several datasets and shown to facilitate GAN training and improve generated image quality. The code is publicly available for use.