Co-operative Generative Adversarial Nets

Generative adversarial nets (GANs) combine the generative model with the discriminative model. With unsupervised training methods, the two types of models mutually improve through the adversarial process. It sets off a new machine learning boom in academia. The final goal of GANs learning is to fit any real-world data distribution. In practice, however, the real-world data distribution is difficult to estimate. The major problem is mode collapse, which may lead to redundancy and non-convergence. To improve the unsupervised generator and eliminate the risk of mode collapse, this paper proposes a novel co-operative network structure for GANs. Multiple generative models are constructed with a co-operative mechanism. It can help generative models to work together and learn from each other during training. In this way, the fitting ability of generators is largely enhanced, furthermore, the quality of generated data is eventually upgraded. Experiments are conducted on three different types of benchmark datasets. Results show that the new model significantly improves image generation, especially for human face pictures. Additionally, the co-operative mechanism can speed up the convergence, improve network's learning efficiency and deduct loss function noise. It also plays a certain role in 3D model generation and suppress the problem of mode collapse. In order to solve the inconsistency between generation model and discriminative model, a dynamic learning method is developed which can dynamically adjust learning frequency. It ultimately reduces unnecessary gradient penalties. Copyright © 2018 Acta Automatica Sinica. All rights reserved.