37 citations · 64 across the 16 of their papers we have counts for
3 papers · 1 filter
PRIME: Prioritizing Interpretability in Failure Mode Extraction
Keivan Rezaei, Mehrdad Saberi, Mazda Moayeri +1
In this work, we study the challenge of providing human-understandable descriptions for failure modes in trained image classification models. Existing works address this problem by…
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
Thomas Fel, Victor Boutin, Mazda Moayeri +5
In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs).…
Text-To-Concept (and Back) via Cross-Model Alignment
Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi +1
We observe that the mapping between an image's representation in one model to its representation in another can be learned surprisingly well with just a linear layer, even across d…