Circle Loss Based Metric Learning For Few Shot Classification
Few shot classification aims to identify new categories from only a handful of labeled examples and has drawn considerable attention in machine learning. An efficient and effective approach for solving few shot classification has featured episodic training based metric learning which attempts to learn a task generic embedding space on extremely large episodes, each with a small labeled support set and its corresponding query set. Distances between embeddings of examples from the query set and support set are then used as a notion of similarity to do classification. This paper first adds a margin between different classes to increase the inter class distance in the embedding space(we call this method ML FSC) Furthermore, considering that a similarity score that deviates far from the optimum should be emphasized, this paper introduces the circle loss into the episodic training based metric learning, which simply re weights each similarity to highlight the less optimized similarity scores, contributing to a more discriminative embedding learning(we call this method CL FSC) We conduct comprehensive experiments to validate our method and we set new state of the art performance on three popular few shot classification benchmarks, namely omniglot, miniImageNet and tieredImageNet.
2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA)