1 citations · 1 across the 4 of their papers we have counts for
4 papers
Learning Less Generalizable Patterns with an Asymmetrically Trained Double Classifier for Better Test-Time Adaptation
Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall +2
Deep neural networks often fail to generalize outside of their training distribution, in particular when only a single data domain is available during training. While test-time ada…
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…
Swapping Semantic Contents for Mixing Images
Rémy Sun, Clément Masson, Gilles Hénaff +2
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bot…
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization
Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall +2
Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of the training data. The issue can be mitigated by using unlabeled data fr…