  One-shot image classification aims to train image classifiers over the
dataset with only one image per category. It is challenging for modern deep
neural networks that typically require hundreds or thousands of images per
class. In this paper, we adopt metric learning for this problem, which has been
applied for few- and many-shot image classification by comparing the distance
between the test image and the center of each class in the feature space.
However, for one-shot learning, the existing metric learning approaches would
suffer poor performance because the single training image may not be
representative of the class. For example, if the image is far away from the
class center in the feature space, the metric-learning based algorithms are
unlikely to make correct predictions for the test images because the decision
boundary is shifted by this noisy image. To address this issue, we propose a
simple yet effective regression model, denoted by RestoreNet, which learns a
class agnostic transformation on the image feature to move the image closer to
the class center in the feature space. Experiments demonstrate that RestoreNet
obtains superior performance over the state-of-the-art methods on a broad range
of datasets. Moreover, RestoreNet can be easily combined with other methods to
achieve further improvement.
