Distribution constraining for combating mode collapse in generative adversarial networks

. Image synthesis is a critical technique in the image processing field. Recently, generative adversarial networks (GANs) have played a significant role in synthesis tasks. However, the issue of mode collapse remains a major challenge in GANs, which limits their potential applications. We propose a method to address the mode collapse problem. Our approach focuses on minimizing the divergence between the distributions of real and generated features, thereby reducing the learning pressure on the discriminator. An advantage of our method is that it does not require prior knowledge or manual design. Additionally, it can be easily incorporated into state-of-the-art frameworks across various domains. Experimental results demonstrate the effectiveness and competitive performance of our proposed method.