A Generation Adversarial Network Based on Multi-Condition Confrontation and Gradient Optimization

Aiming at the problem of pattern collapse, this paper starts from the idea of forcing each generator to generate different pattern data in a multi-generator game, and proposes a multi-generator-based generation confrontation network, named improved multi-generator generative adversarial nets (IMGAN). IMGAN uses parameter sharing between multiple generators to speed up training, and at the same time uses the last layer of independent training to weaken the impact of parameter identity; introduces a regular penalty term to make the loss function better satisfy Lipschitz continuousness, which avoids the effect of gradient disappearance to a certain extent; and introduces a hyperparameter to solve the disparity problem caused by multiple loss functions and avoid excessive bias toward one of the gradient directions. At last, we verify the performance of our model through comparative experiments on multiple data sets.