With the increasing demand for digital art, animation, and games, facial generation for anime characters has attracted growing research interest in recent years, which aims to build models to automatically generate unique and high-quality character images. Thanks to the rapid advancement of deep learning techniques, particularly generative adversarial networks, GAN-based image generation methods have continuously achieved breakthroughs in generation effectiveness and speed. Focusing on generating realistic anime face images, this paper proposes an anime character face image generation model based on GANs, which integrates Bath Normalization and Dropout to maintain strong stability and avoid overfitting. Comprehensive experiments show the efficacy of the proposed method, which can achieve high diversity in facial features and styles while maintaining visual coherence
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