A Denoising Generative Adversarial Network Based On Enhanced Feature Mapping Of Data Augmentation For Image Synthesis - Chen, Li (2024)
Generative adversarial networks (GANs) have become a significant research topic in deep learning for image synthesis. GANs can produce diverse and high-quality results through the collaboration between the generator and discriminator. However, building a robust and stable GANs model remains a significant challenge. Previous research has attempted to enhance the original GANs by utilizing various algorithms to measure divergence between data distributions, implementing different network structures, or combining them with other structures to achieve better results. But these improvements were often limited to a single perspective. This research introduces the Denoising Feature Mapping GAN (DNFM-GAN), a GAN variant that enhances the stability of the model's training by improving both the generator and discriminator components. Specifically, the generator's ability is enhanced by adding data with noise as an extra input. This requires the generator to learn how to generate images from partially damaged data, leading to better representations learned from the data. To ensure the generator's stability and robustness, it is important to minimize the volatility caused by generator loss. Additionally, generating two types of data,