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Prototypical network (PN) is a simple yet effective few shot learningstrategy. It is a metric-based meta-learning technique where classification isperformed by computing Euclidean distances to prototypical representations ofeach class. Conventional PN attributes equal importance to all samples andgenerates prototypes by simply averaging the support sample embeddingsbelonging to each class. In this work, we propose a novel version of PN thatattributes weights to support samples corresponding to their influence on thesupport sample distribution. Influence weights of samples are calculated basedon maximum mean discrepancy (MMD) between the mean embeddings of sampledistributions including and excluding the sample. Further, the influence factorof a sample is measured using MMD based on the shift in the distribution in theabsence of that sample.

IPNET:Influential Prototypical Networks for Few Shot Learning