2 citations · 4 across the 3 of their papers we have counts for
7 papers
Test-Time Adaptation with Principal Component Analysis
Thomas Cordier, Victor Bouvier, Gilles Hénaff +1
Machine Learning models are prone to fail when test data are different from training data, a situation often encountered in real applications known as distribution shift. While sti…
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
Etienne Bennequin, Victor Bouvier, Myriam Tami +2
Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes en…
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data. I…
Target Consistency for Domain Adaptation: when Robustness meets Transferability
Yassine Ouali, Victor Bouvier, Myriam Tami +1
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation. By investigating the robustness of…
Robust Domain Adaptation: Representations, Weights and Inductive Bias
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence of Domain Invariant Representations (IR) has improved drastically the tra…
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets
Victor Bouvier, Philippe Very, Céline Hudelot +1
Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such represent…