All you need is a good representation: A multi-level and classifier-centric representation for few-shot learning
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The main problems of few-shot learning are how to learn a generalized representation and how to construct discriminant classifiers with few-shot samples. We tackle both issues by learning a multi-level representation with a classifier-centric constraint. We first build the multi-level representation by combining three different levels of information: local, global, and higher-level. The resulting representation can characterize new concepts with different aspects and present more universality. To overcome the difficulty of generating classifiers by several shot features, we also propose a classifier-centric loss for learning the representation of each level, which forces samples to be centered on their respective classifier weights in the feature space. Therefore, the multi-level representation learned with classifier-centric constraint not only can enhance the generalization ability, but also can be used to construct the discriminant classifier through a small number of samples. Experiments show that our proposed method, without training or fine-tuning on novel examples, can outperform the current state-of-the-art methods on two low-shot learning datasets. We further show that our approach achieves a significant improvement over baseline method in cross-task validation, and demonstrate its superiority in alleviating the domain shift problem.
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