Improving GAN Performance Using Confidence-Aware Discrimination

Abstract

Generative Adversarial Networks (GAN) involve the competition between a generator and a discriminator. The large variance of training data can bring difficulty to GAN resulting in mode collapse, training instability and low quality. As the distribution of generated samples evolves, the discriminator will have different levels of confidence for its predictions. To mitigate these problems, we introduce confidence-aware discrimination (CAD) to guide the training and sampling of GANs. To adapt confidence estimation for GANs, we design a new architecture based on StyleGAN2, and propose a confidence-aware adversarial loss. Extensive experiments are conducted on face, scene and object generation benchmarks. In the training stage, CAD can progressively learn the training data with the guide of confidence estimation and lead to a better convergence. In the inference stage, the confidence score can provide a new dimension of metric to assess the quality of generated samples and can further improve the performance by resampling and finetuning.