  The use of a few examples for each class to train a predictive model that can
be generalized to novel classes is a crucial and valuable research direction in
artificial intelligence. This work addresses this problem by proposing a
few-shot learning (FSL) algorithm called proxy network under the architecture
of meta-learning. Metric-learning based approaches assume that the data points
within the same class should be close, whereas the data points in the different
classes should be separated as far as possible in the embedding space. We
conclude that the success of metric-learning based approaches lies in the data
embedding, the representative of each class, and the distance metric. In this
work, we propose a simple but effective end-to-end model that directly learns
proxies for class representative and distance metric from data simultaneously.
We conduct experiments on CUB and mini-ImageNet datasets in 1-shot-5-way and
5-shot-5-way scenarios, and the experimental results demonstrate the
superiority of our proposed method over state-of-the-art methods. Besides, we
provide a detailed analysis of our proposed method.
