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cs.CV2023
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations
Mikołaj Sacha, Bartosz Jura, Dawid Rymarczyk +3
Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations. However, their similarity maps are calculated in the penultimate networ…
cs.CV2023★ 1 cited
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
Mikołaj Sacha, Dawid Rymarczyk, Łukasz Struski +2
We introduce ProtoSeg, a novel model for interpretable semantic image segmentation, which constructs its predictions using similar patches from the training set. To achieve accurac…