Mean Field Game GAN
This addresses training challenges in GANs for generative modeling, but appears incremental as it builds on existing MFG and GAN concepts.
The paper tackles the problem of training generative adversarial networks (GANs) by proposing a mean field games (MFGs) based framework, which avoids the Lipschitz-1 constraint and is validated as correct and efficient in experiments.
We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework. To be specific, we utilize the Hopf formula in density space to rewrite MFGs as a primal-dual problem so that we are able to train the model via neural networks and samples. Our model is flexible due to the freedom of choosing various functionals within the Hopf formula. Moreover, our formulation mathematically avoids Lipschitz-1 constraint. The correctness and efficiency of our method are validated through several experiments.