Research on anime avatar generation technology based on Wasserstein generative adversarial networks

Currently, there is a scarcity of datasets for anime avatars, compounded by copyright concerns. To address this issue, this paper proposes a method for generating anime avatars. Firstly, a wasserstein generative adversarial networks(WGAN) network is employed to construct the model, with RMSprop chosen as the optimizer. Subsequently, publicly available anime avatar samples are randomly collected for model training, and model hyperparameters are determined through grid search and personal experience. Finally, during the experimental simulation phase, after 300 epochs of training, the training errors of the generator and discriminator decrease and stabilize, and the generated image samples closely resemble real images. Experimental results demonstrate that this method can generate realistic anime avatars with excellent performance.