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arXiv · 2112.02450

Adaptive Feature Interpolation for Low-Shot Image Generation

Mengyu Dai·Haibin Hang·Xiaoyang Guo
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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 high-quality samples without need of label information. 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 fully unsupervised and data-driven augmentation method. Experiments on few-shot generation tasks show the proposed method significantly improve results from strong baselines with hundreds of training samples.

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Mengyu Dai, Haibin Hang, Xiaoyang Guo. 2021-12-04. Adaptive Feature Interpolation for Low-Shot Image Generation. https://arxiv.org/abs/2112.02450

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