Implicit Data Augmentation Using Feature Interpolation for Diversified Low-Shot Image Generation

• URL: http://arxiv.org/abs/2112.02450v1
• Date: Sat, 4 Dec 2021 23:55:46 GMT
• Title: Implicit Data Augmentation Using Feature Interpolation for Diversified Low-Shot Image Generation
• Authors: Mengyu Dai, Haibin Hang and Xiaoyang Guo
• Abstract summary: Training of generative models can easily diverge in low-data setting. We propose a novel implicit data augmentation approach which facilitates stable training and synthesize diverse samples.
• Score: 11.4559888429977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training of generative models especially Generative Adversarial Networks can easily diverge in low-data setting. To mitigate this issue, we propose a novel implicit data augmentation approach which facilitates stable training and synthesize diverse samples. Specifically, we view the discriminator as a metric embedding of the real data manifold, which offers proper distances between real data points. We then utilize information in the feature space to develop a data-driven augmentation method. We further bring up a simple metric to evaluate the diversity of synthesized samples. Experiments on few-shot generation tasks show our method improves FID and diversity of results compared to current methods, and allows generating high-quality and diverse images with less than 100 training samples.

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