6 papers · 1 filter
DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
Adam Wróbel, Siddhartha Gairola, Jacek Tabor +3
Vision Transformers (ViTs) have become a dominant architecture in computer vision, yet producing stable and high-resolution attribution maps for these models remains challenging. A…
ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
Mikołaj Janusz, Adam Wróbel, Bartosz Zieliński +1
Prototypical parts-based models offer a "this looks like that" paradigm for intrinsic interpretability, yet they typically struggle with ImageNet-scale generalization and often req…
SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
Viktar Dubovik, Łukasz Struski, Jacek Tabor +1
Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparen…
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
Tomasz Michalski, Adam Wróbel, Andrea Bontempelli +6
Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "thi…
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…
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…