Research on Adversarial Learning-based Image Generation Algorithm for Artistic Creation

Aiming at the problems of insufficient style migration accuracy, weak detail expression and poor training stability in image generation for art creation, this paper proposes an art image generation algorithm based on adversarial learning. The modeling and training process of the generative network is systematically optimized by constructing a multi-level feature extraction mechanism, an adaptive style encoding method and a cascading high-resolution generation strategy. The composite style migration loss and adversarial training optimization techniques are introduced to effectively improve the structural fidelity and stylistic consistency of the generated images. A large number of experiments prove that the proposed method is better than the existing mainstream models in terms of image quality, artistic perception and computational efficiency, and has good potential for application and scalability.