Image Generation From Small Datasets via Batch Statistics Adaptation

Code

• github.com/nogu-atsu/small-dataset-image-generation
  OfficialIn paperpytorch★ 0
• github.com/MiaoyunZhao/GANTransferLimitedData
  pytorch★ 59
• github.com/apple2373/PyTorch-SmallGAN
  pytorch★ 0
• github.com/mevius6/smallgan
  pytorch★ 0

Abstract

Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such generative models requires a large dataset. To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain. Using such prior knowledge, the model can generate images leveraging some common sense that cannot be acquired from a small dataset. In this work, we propose a novel method focusing on the parameters for batch statistics, scale and shift, of the hidden layers in the generator. By training only these parameters in a supervised manner, we achieved stable training of the generator, and our method can generate higher quality images compared to previous methods without collapsing, even when the dataset is small (~100). Our results show that the diversity of the filters acquired in the pre-trained generator is important for the performance on the target domain. Our method makes it possible to add a new class or domain to a pre-trained generator without disturbing the performance on the original domain.

Tasks

Benchmark Results

Reproductions