In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
The goal of generative models is to match real data distribution @math with generated data distribution @math . Thus, minimizing differences between two distributions is a crucial point for training generative models. Goodfellow introduced an adversarial framework (GAN) , @cite_16 which is capable of learning deep generative models by minimizing the Jensen-Shannon Divergence between @math and @math . This optimization problem can be described as a minmax game between the generator @math , which learns how to generate samples which resemble real data, and a discriminator @math , which learns to discriminate between real and fake data. Throughout this process, @math indirectly learns how to model @math by taking samples @math from a fixed distribution @math (e.g. Gaussian) and forcing the generated samples @math to match @math . The objective loss function is defined as
@cite_16
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We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.