Title: MMGAN: Manifold Matching Generative Adversarial Network,
Abstract: It is well-known that GANs are difficult to train, and several different
techniques have been proposed in order to stabilize their training. In this
paper, we propose a novel training method called manifold-matching, and a new
GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds
representing the vector representations of real and fake images. If these two
manifolds match, it means that real and fake images are statistically
identical. To assist the manifold-matching task, we also use i) kernel tricks
to find better manifold structures, ii) moving-averaged manifolds across
mini-batches, and iii) a regularizer based on correlation matrix to suppress
mode collapse.
We conduct in-depth experiments with three image datasets and compare with
several state-of-the-art GAN models. 32.4% of images generated by the proposed
MMGAN are recognized as fake images during our user study (16% enhancement
compared to other state-of-the-art model). MMGAN achieved an unsupervised
inception score of 7.8 for CIFAR-10.