5 citations · 6 across the 9 of their papers we have counts for
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cs.CV2022★ 5 cited
Improving Zero-Shot Models with Label Distribution Priors
Jonathan Kahana, Niv Cohen, Yedid Hoshen
Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate s…
cs.CV2022★ 1 cited
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
Niv Cohen, Jonathan Kahana, Yedid Hoshen
Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.…
cs.CV2022
A Contrastive Objective for Learning Disentangled Representations
Jonathan Kahana, Yedid Hoshen
Learning representations of images that are invariant to sensitive or unwanted attributes is important for many tasks including bias removal and cross domain retrieval. Here, our o…