4 papers
Towards Visually Explaining Statistical Tests with Applications in Biomedical Imaging
Masoumeh Javanbakhat, Piotr Komorowski, Dilyara Bareeva +3
Deep neural two-sample tests have recently shown strong power for detecting distributional differences between groups, yet their black-box nature limits interpretability and practi…
Manipulating Feature Visualizations with Gradient Slingshots
Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke +5
Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a g…
Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers
Johanna Vielhaben, Dilyara Bareeva, Jim Berend +2
Vision transformers (ViTs) can be trained using various learning paradigms, from fully supervised to self-supervised. Diverse training protocols often result in significantly diffe…
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
Dilyara Bareeva, Galip Ãmit Yolcu, Anna Hedström +4
In recent years, training data attribution (TDA) methods have emerged as a promising direction for the interpretability of neural networks. While research around TDA is thriving, l…