IPNET:Influential Prototypical Networks for Few Shot Learning
This work addresses few-shot learning for scenarios with limited labeled data, representing an incremental improvement over existing prototypical networks.
The authors tackled the problem of few-shot learning by proposing a novel prototypical network that weights support samples based on their influence on the distribution, using maximum mean discrepancy to calculate these weights, resulting in improved classification performance.
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. Further, the influence factor of a sample is measured using MMD based on the shift in the distribution in the absence of that sample.