  Prototypical network (PN) is a simple yet effective few shot learning
strategy. It is a metric-based meta-learning technique where classification is
performed by computing Euclidean distances to prototypical representations of
each class. Conventional PN attributes equal importance to all samples and
generates prototypes by simply averaging the support sample embeddings
belonging to each class. In this work, we propose a novel version of PN that
attributes weights to support samples corresponding to their influence on the
support sample distribution. Influence weights of samples are calculated based
on maximum mean discrepancy (MMD) between the mean embeddings of sample
distributions including and excluding the sample. Comprehensive evaluation of
our proposed influential PN (IPNet) is performed by comparing its performance
with other baseline PNs on three different benchmark dermatological datasets.
IPNet outperforms all baseline models with compelling results across all three
datasets and various N-way, K-shot classification tasks. Findings from
cross-domain adaptation experiments further establish the robustness and
generalizability of IPNet.
