Semi-Supervised Learning

Semantic augmentation by mixing contents for semi-supervised learning
Leveraging unlabeled examples is a crucial issue for boosting performances in semi-supervised learning. In this work, we introduce the …
Remy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
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Semantic augmentation by mixing contents for semi-supervised learning
Swapping Semantic Contents for Mixing Images
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of …
Remy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
PDF Cite Project Project
Swapping Semantic Contents for Mixing Images
Semantic augmentation by mixing contents for semi-supervised learning
Leveraging unlabeled examples is a crucial issue for boosting performances in semi-supervised learning. In this work, we introduce the …
Remy Sun, Clément Masson, Gilles Hénaff, Nicolas Thome, Matthieu Cord
PDF Project