1.6k citations
- Université Gustave EiffelFR201 papers
- Centre National de la Recherche ScientifiqueFR89 papers
- Institutt for Grafiske MedierNO25 papers
- Université Paris CitéFR16 papers
- École nationale des ponts et chausséesFR15 papers
- UniLaSalle Amiens (ESIEE-Amiens)FR13 papers
- École Normale Supérieure - PSLFR12 papers
- Institut de Recherche en Informatique FondamentaleFR12 papers
- Université Paris-SaclayFR11 papers
- IFP Énergies nouvellesFR8 papers
- Systèmes d'Elevage Méditerranéens et Tropicaux - Laboratoire de Recherche sur le Développement de l'ElevageFR8 papers
- Centre Inria de SaclayFR7 papers
8 papers · 2 filters
Improving Plasticity in Online Continual Learning via Collaborative Learning
Maorong Wang, Nicolas Michel, Ling Xiao +1
Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL…
Class Uncertainty: A Measure to Mitigate Class Imbalance
Z. S. Baltaci, K. Oksuz, S. Kuzucu +5
Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long…
SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features
Sarah Almeida Carneiro, Giovanni Chierchia, Jean Charléty +2
Although traffic is one of the massively collected data, it is often only available for specific regions. One concern is that, although there are studies that give good results for…
Exact and general decoupled solutions of the LMC Multitask Gaussian Process model
Olivier Truffinet, Karim Ammar, Jean-Philippe Argaud +1
The Linear Model of Co-regionalization (LMC) is a very general multitask gaussian process model for regression or classification. While its expressiveness and conceptual simplicity…
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning
Grégoire Petit, Michael Soumm, Eva Feillet +4
Class-Incremental Learning (CIL) aims to build classification models from data streams. At each step of the CIL process, new classes must be integrated into the model. Due to catas…
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…