  Mode collapse is a critical problem in training generative adversarial
networks. To alleviate mode collapse, several recent studies introduce new
objective functions, network architectures or alternative training schemes.
However, their achievement is often the result of sacrificing the image
quality. In this paper, we propose a new algorithm, namely a manifold guided
generative adversarial network (MGGAN), which leverages a guidance network on
existing GAN architecture to induce generator learning all modes of data
distribution. Based on extensive evaluations, we show that our algorithm
resolves mode collapse without losing image quality. In particular, we
demonstrate that our algorithm is easily extendable to various existing GANs.
Experimental analysis justifies that the proposed algorithm is an effective and
efficient tool for training GANs.
