prototype learning

**Prototype Learning** is an **interpretable ML approach where the model learns a set of representative examples (prototypes) and classifies new inputs based on their similarity to these prototypes** — providing explanations of the form "this looks like prototype X" which are naturally intuitive. **How Prototype Learning Works** - **Prototypes**: The model learns $k$ prototype feature vectors per class during training. - **Similarity**: For a new input, compute similarity (L2 distance, cosine) to all prototypes in the learned feature space. - **Classification**: Predict the class based on weighted similarities to prototypes. - **Visualization**: Each prototype can be projected back to input space or matched to nearest real examples. **Why It Matters** - **Natural Explanations**: "This is class A because it looks like prototype A3" — matches human reasoning. - **ProtoPNet**: Prototypical Part Networks learn part-based prototypes — "this bird has a beak like prototype X." - **Trustworthy AI**: Prototype-based explanations are more intuitive than feature attribution methods. **Prototype Learning** is **classification by example** — explaining predictions through similarity to learned representative examples that humans can examine.