3 accepted papers
Causal representation learning has attracted significant research interest during the past few years, as a means for improving model generalization and robustness. Causal representations of interventional image pairs (also called ``actionable counterfactuals'' in the literature), have the property t…
Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even state-of-the-art training algorithms. In this paper we observe that the initial iterations of model training play a key role…
Mitigating biases in computer vision models is an essential step towards trustworthy artificial intelligence systems. Existing bias mitigation methods are limited to predefined biases, preventing their use in visual datasets where multiple, possibly unknown biases exist. To address this limitation,…