Poster

Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis

Bingchen Liu · Yizhe Zhu · Kunpeng Song · Ahmed Elgammal

Keywords: [ self-supervised learning ] [ deep learning ] [ generative model ] [ few-shot learning ] [ unsupervised learning ] [ generative adversarial network ] [ image synthesis ]

Abstract:

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for GAN with minimum computing cost. We propose a light-weight GAN structure that gains superior quality on 1024^2 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples. Two technique designs constitute our work, a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder. With thirteen datasets covering a wide variety of image domains (The datasets and code are available at https://github.com/odegeasslbc/FastGAN-pytorch), we show our model's superior performance compared to the state-of-the-art StyleGAN2, when data and computing budget are limited.

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