Koryna Lewandowska — accepted AI-conference papers

2025

LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision

Mateusz Pach, Koryna Lewandowska, Jacek Tabor, Bartosz Michał Zieliński, Dawid Damian Rymarczyk

ICLR 2025poster

Prototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions. They follow the this looks like that reasoning, representing each prototypical part with patches from training images. However, a single image pat…

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2022

Interpretable Image Classification with Differentiable Prototypes Assignment

Dawid Rymarczyk, Łukasz Struski, Michał Górszczak, Koryna Lewandowska, Jacek Tabor, Bartosz Zieliński

ECCV 2022poster

"Existing prototypical-based models address the black-box nature of deep learning. However, they are sub-optimal as they often assume separate prototypes for each class, require multi-step optimization, make decisions based on prototype absence (so-called negative reasoning process), and derive vagu…

Cited by 121 Source ↗PDF ↗code ↗Scholar ↗