24 citations · 25 across the 4 of their papers we have counts for
3 papers · 1 filter
LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision
Mateusz Pach, Dawid Rymarczyk, Koryna Lewandowska +2
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…
Interpretable Image Classification with Differentiable Prototypes Assignment
Dawid Rymarczyk, Łukasz Struski, Michał Górszczak +3
We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes. The training is more straightforward than in the existing metho…
Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations
Witold Oleszkiewicz, Dominika Basaj, Igor Sieradzki +5
Recently introduced self-supervised methods for image representation learning provide on par or superior results to their fully supervised competitors, yet the corresponding effort…