4 papers · 1 filter
OMENN: One Matrix to Explain Neural Networks
Adam Wróbel, MikoÅaj Janusz, Bartosz ZieliÅski +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…
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
Jan Olszewski, Dawid Rymarczyk, Piotr Wójcik +2
Active Visual Exploration (AVE) optimizes the utilization of robotic resources in real-world scenarios by sequentially selecting the most informative observations. However, modern…
Revisiting FunnyBirds evaluation framework for prototypical parts networks
Szymon OpÅatek, Dawid Rymarczyk, Bartosz ZieliÅski
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…
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…