Noise Homogenization via Multi-Channel Wavelet Filtering for High-Fidelity Sample Generation in Gans

Abstract

In a typical Generative Adversarial Network (GAN), a noise is sampled to generate fake samples via a series of convolutional operations after random initialization. However, current GANs merely rely on the pixel space to sample the noise, increasing the difficulty of approaching the target distribution. Fortunately, the long proven wavelet transformation is able to decompose multiple spectral information from the images. In this work, we propose a novel multi-channel wavelet-based filtering method for GANs, to cope with this problem. The proposed WaveletNet embeds a wavelet deconvolution layer in the generator to take advantage of the wavelet deconvolution. By learning a filter with multiple channels, or multiple convolutional filters, it can efficiently homogenize the sampled noise via an averaging operation, to generate high-fidelity samples. We conducted benchmark experiments on the Fashion-MNIST, KMNIST, and SVHN datasets through an open GAN benchmark tool. The results showed that WaveletGAN has excellent performance in generating high-fidelity samples.