All you need is a good representation: A multi level and classifier centric representation for few shot learning
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.
ArXiv