Class Regularization: Improve Few-shot Image Classification by Reducing Meta Shift

Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the meta-learning process where episodic tasks are formed to train a model that can adapt to class change. However, these tasks are independent to each other and existing works mainly rely on limited samples of individual support set in a single meta task. This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set. To avoid this problem, we propose a concise Class Regularization Network which aggregates the embedding features of all samples in the entire training set and further regularizes the generated class descriptor. The key is to train a class encoder and decoder structure that can encode the embedding sample features into a class domain with trained class basis, and generate a more stable and general class descriptor from the decoder. We evaluate our work by extensive comparisons with previous methods on two benchmark datasets (MiniImageNet and CUB). The results show that our method achieves state-of-the-art performance over previous work.