DuC-GAN:a Novel Model for Enhancing GAN Training Stability
We propose a new model,called"dual-cycle generative adversarial networks(DuC-GAN)"to enhance the stability of training in generative adversarial networks(GAN).The framework addresses the issue of training instability in GAN by introducing an additional cycle between the generator and discriminator.This new cycle is composed of a frozen original discriminator and a new discriminator,both of which are trained together with the generator and switched based on the generator's performance.Testing on multiple datasets has proved that the proposed framework significantly improves the performance and training stability of GAN compared to available model,achieving faster convergence and better generation quality.
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