20 citations · 26 across the 3 of their papers we have counts for
7 papers
Insights from the Future for Continual Learning
Arthur Douillard, Eduardo Valle, Charles Ollion +2
Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this…
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
Arthur Douillard, Matthieu Cord, Charles Ollion +2
Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learn…
DualDis: Dual-Branch Disentangling with Adversarial Learning
Thomas Robert, Nicolas Thome, Matthieu Cord
In computer vision, disentangling techniques aim at improving latent representations of images by modeling factors of variation. In this paper, we propose DualDis, a new auto-encod…
HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
Thomas Robert, Nicolas Thome, Matthieu Cord
In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called…
SHADE: Information Based Regularization for Deep Learning
Michael Blot, Thomas Robert, Nicolas Thome +1
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The o…
SHADE: Information-Based Regularization for Deep Learning
Michael Blot, Thomas Robert, Nicolas Thome +1
Regularization is a big issue for training deep neural networks. In this paper, we propose a new information-theory-based regularization scheme named SHADE for SHAnnon DEcay. The o…