3 papers
cs.LG2024
OMENN: One Matrix to Explain Neural Networks
Adam Wróbel, Mikołaj Janusz, Bartosz Zieliński +1
Deep Learning (DL) models are often black boxes, making their decision-making processes difficult to interpret. This lack of transparency has driven advancements in eXplainable Art…
cs.CV2024
Revisiting FunnyBirds evaluation framework for prototypical parts networks
Szymon Opłatek, Dawid Rymarczyk, Bartosz Zieliński
Prototypical parts networks, such as ProtoPNet, became popular due to their potential to produce more genuine explanations than post-hoc methods. However, for a long time, this pot…
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…