Stabilizing GANs with Soft Octave Convolutions

Abstract: Motivated by recently published methods using frequency decompositions 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. We also show, that the use of the proposed soft octave convolutions reduces common artifacts in the frequency domain of generated images. Our approach is orthogonal and complementary to existing stabilization methods and can simply be plugged into any CNN based GAN architecture. Experiments on the CelebA dataset show the effectiveness of the proposed method.