most citedEncouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

2 papers · 1 filter

cs.CV2022

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…

cs.CV2021★ 1 cited

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…