Image Generation from Small Datasets via Batch Statistics Adaptation

DOI DOI DOI Web Site オープンアクセス
  • NOGUCHI Atsuhiro
    Graduate School of Information Science and Technology, the University of Tokyo
  • HARADA Tatsuya
    Research Center for Advanced Science and Technology, the University of Tokyo RIKEN AIP

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説明

<p>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.</p>

収録刊行物

  • 日本画像学会誌

    日本画像学会誌 59 (6), 607-616, 2020-12-10

    一般社団法人 日本画像学会

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