Improved WGAN for Image Generation Methods

For the problem of generating high-quality and diverse images, an image generation method combining residual module, spectral parametric normalization, and self-attention mechanism is proposed to be applied in WGAN networks. The specific improvement of the method is to introduce the residual module into the generator and discriminator networks to better capture the deep image information. The spectral parametric normalization technique is also applied to each convolutional layer of the residual block to improve the stability of the image generation process. The self-attention mechanism is introduced into the generator to enable the network to learn in a targeted manner and generate higher-quality images. The experimental results demonstrate that the combined application of these techniques can effectively solve the challenge of generating image samples, obtain stable and diverse data samples, generate better results than the original WGAN method and DCGAN method, and use the generated data samples as the dataset for expanding the classification experiments, which improves the recognition accuracy of the image classification network to a certain extent.