prototypical-networks-classify-by-prototype-distance

Summary

The model decides which category a new input belongs to by measuring how close it sits to a learned "center" for each category, rather than by a weighted combination of raw features. This makes the reasoning geometric and interpretable: you can point to exactly which category's center a prediction was pulled toward, and the approach works well even when you only have a handful of examples per class.