concept activation vectors 1concept-based explanations 1contrastive attribution 1embedding perturbation 1explainability 1explainable AI 1image classification 1image similarity 1representation learning 1visual concepts 1
From the 2 of 3 linked papers with an AI index.
3 papers
cs.CV2026
Explaining Image Similarity with Automatically Extracted Concept Activation Vectors
Isaac Roberts, Petra Bevandic, Alexander Schulz +1
The paper proposes a model‑agnostic method that uses automatically discovered concept activation vectors to explain why two images are considered similar, by perturbing embeddings…
cs.LG2026
Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations
Roel Visser, Isaac Roberts, Barbara Hammer
The paper proposes Contrastive Concept Importance (CCI), a method that attributes the logit margin between a target and a foil class to automatically extracted visual concepts, pro…
cs.LG2025
Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
Isaac Roberts, Alexander Schulz, Sarah Schroeder +2
Uncertainty in machine learning refers to the degree of confidence or lack thereof in a model's predictions. While uncertainty quantification methods exist, explanations of uncerta…