1 citations · 3 across the 6 of their papers we have counts for
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Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Nicolas Michel, Romain Negrel, Giovanni Chierchia +1
Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agen…
New metrics for analyzing continual learners
Nicolas Michel, Giovanni Chierchia, Romain Negrel +2
Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, i…
Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning
Nicolas Michel, Giovanni Chierchia, Romain Negrel +1
We use the maximum a posteriori estimation principle for learning representations distributed on the unit sphere. We propose to use the angular Gaussian distribution, which corresp…