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One-shot image classification aims to train image classifiers over thedataset with only one image per category. It is challenging for modern deepneural networks that typically require hundreds or thousands of images perclass. In this paper, we adopt metric learning for this problem, which has beenapplied for few- and many-shot image classification by comparing the distancebetween the test image and the center of each class in the feature space.However, for one-shot learning, the existing metric learning approaches wouldsuffer poor performance because the single training image may not berepresentative of the class. For example, if the image is far away from theclass center in the feature space, the metric-learning based algorithms areunlikely to make correct predictions for the test images because the decisionboundary is shifted by this noisy image. To address this issue, we propose asimple yet effective regression model, denoted by RestoreNet, which learns aclass agnostic transformation on the image feature to move the image closer tothe class center in the feature space. Experiments demonstrate that RestoreNetobtains superior performance over the state-of-the-art methods on a broad rangeof datasets. Moreover, RestoreNet can be easily combined with other methods toachieve further improvement.

One-Shot Image Classification by Learning to Restore Prototypes