Few-shot Constraint Enhancement Based on Generative Adversarial Networks

A constrained theoretical model for Generative Adversarial Networks (GANs) is proposed. To address issues such as overfitting, convergence difficulties, and mode collapse in the GAN training process, a GAN structure and process constrained training based on Directed Graphical Models(DGM) is first introduced to solve the instability and quality issues of generated samples. Then, a static constraint method is proposed, which calculates the similarity of interpretable measurement (EMS) and final classification metrics of generated data on different classifiers by setting the topology of D and G, and measures the constraint strength through EMS to suppress overfitting during the generation process. Furthermore, the constraint of label sharing features and weight updates effectively reduces the probability of mode collapse by appropriately constraining the functionality of label information in generation. The constraint of GAN solves the problem of effective sample enhancement.