A Sparse Substitute for Deconvolution Layers in GANs

摘要

Generative adversarial networks are useful tools in image generation task, but training and running them are relatively slow due to the large amount parameters introduced by their generators. In this paper, S-Deconv, a sparse drop-in substitute for deconvolution layers, is proposed to alleviate this issue. S-Deconv decouples reshaping input tensor from reweighing it by first processing it with a sparse fixed filter into desired form then reweighing them using learnable one. By doing so, S-Deconv reduces the numbers of learnable and total parameters with sparsity. Our experiments on Fashion-MNIST, CelebA and Anime-Faces verify the feasibility of our method. We also give another interpretation of our method from the perspective of regularization.