Image Generation from Small Datasets via Batch Statistics Adaptation

抄録

In this review, we introduce our proposed novel method for training an image generation model from only a small number of unknown category images. Image generation models can learn the distribution of images from the training images and generate new images according to the distribution. Recent advances in image generation models have made it possible to generate high-quality images;however, the need for large datasets for training has limited the application of such models. Therefore, in this study, we realized an image generation from a small number of images by reusing the feature representations acquired by the pre-trained image generator on a large dataset and learning only how to combine the feature representations. The proposed method focuses on batch statistics that contribute to this combination and trains only these parameters. This method enabled us to generate higher quality images from a small dataset (less than 100 images) compared to conventional methods.