Improving the Training of Data-Efficient GANs via Quality Aware Dynamic Discriminator Rejection Sampling

Data Efficient Generative Adversarial Networks (DE-GANs) have become more and more popular in recent years. Existing methods apply data augmentation, noise injection and pre-trained models to maximumly increase the number of training samples thus improving the training of DE-GANs. However, none of these methods considers the sample quality during training, which can also significantly influence the DE-GANs training. Focusing on the sample quality during training, in this paper, we are the first to incorporate discriminator rejection sampling (DRS) into the training process and introduce a novel method, called quality aware dynamic discriminator rejection sampling (QADDRS). Specifically, QADDRS consists of two steps: (1) the sample quality aware step, which aims to obtain the sorted critic scores, i.e., the ordered discriminator outputs, on real/fake samples in the current training stage; (2) the dynamic rejection step that obtains dynamic rejection number $N$, where $N$ is controlled by the overfitting degree of $D$ during training. When updating the parameters of the $D$, the $N$ high critic score real samples and the $N$ low critic score fake samples in the minibatch are rejected based on the overfitting degree of $D$ dynamically. As a result, QADDRS can avoid $D$ becoming overly confident in distinguishing both real and fake samples, thereby alleviating the overfitting of $D$ issue during training. Extensive experiments on several datasets demonstrate that QADDRS can achieve better performance across different DE-GANs and deliver state-of-the-art performance compared with other GANs and diffusion models.