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

Code

• github.com/odegeasslbc/FastGAN-pytorch
  OfficialIn paperpytorch★ 0
• github.com/gaborvecsei/SLE-GAN
  tf★ 70
• github.com/reyllama/mixdl
  pytorch★ 26
• github.com/milmor/self-supervised-gan
  tf★ 6
• github.com/silentz/Towards-Faster-And-Stabilized-GAN-Training-For-High-Fidelity-Few-Shot-Image-Synthesis
  pytorch★ 2
• github.com/palandr1234/exploring-gan-latent-spaces
  pytorch★ 0
• github.com/lucidrains/lightweight-gan
  pytorch★ 0

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*1024 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.

Tasks

Benchmark Results

Reproductions