ProtoSimNet: Exploiting prototype similarity for few-shot learning

Neurocomputing, vol. 702, pp. 134609

Abstract

Many few-shot learning (FSL) approaches exploit Euclidean Distance as the metric, as it provides a stable and well-understood geometric structure for prototype-based classification. Despite these advantages, we observe that Euclidean Distance metric may suffer from a metric-level limitation under few-shot supervision. Meanwhile, with limited supervision, cosine-based scoring often leads to a metric over-smoothing phenomenon, where similarity scores among competing prototypes become overly compressed, producing weak effective margins and less stable decision boundaries, resulting in more ambiguous predictions when distinguishing fine-grained novel categories. In this work, we identify this metric-level limitation and propose a problem-driven prototype similarity framework, termed ProtoSimNet, for FSL which explicitly performs score-level distribution shaping of prototype similarities under data-scarce episodic supervision. ProtoSimNet introduces a cosine sharpening transformation, combined with an elastic regularization, which together enhance discriminability by amplifying the margins between high- and low-confidence prototype similarity scores while regularizing the encoder toward compact and less redundant feature embeddings. Our method strengthens the reliability of cosine-based classification and improves the robustness of prototype matching when only a few labeled samples are available. Experimental results on FSL benchmark datasets show that ProtoSimNet achieves consistent improvements over standard Prototypical Networks and competitive or superior performance compared with representative state-of-the-art methods.

Authors 4

1. Kai He corresponding Aachen LIACS Media Lab

   Leiden University

   Affiliation as printed

   LIACS Media Lab, Leiden University, Leiden, Netherlands

2. Nan Pu

   Hefei University of Technology

   Affiliation as printed

   Lab for Intelligence and vision, Hefei University of Technology, Hefei, China

3. Erwin M. Bakker Aachen LIACS Media Lab

   Leiden University

   Affiliation as printed

   LIACS Media Lab, Leiden University, Leiden, Netherlands

4. Michael S. Lew Aachen LIACS Media Lab

   Leiden University

   Affiliation as printed

   LIACS Media Lab, Leiden University, Leiden, Netherlands