244 citations
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8 papers · 1 filter
Physics-Informed Graph Convolutional Networks: Towards a generalized framework for complex geometries
Marien Chenaud, José Alves, Frédéric Magoulès
Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep L…
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…
Test-Time Adaptation with Principal Component Analysis
Thomas Cordier, Victor Bouvier, Gilles Hénaff +1
Machine Learning models are prone to fail when test data are different from training data, a situation often encountered in real applications known as distribution shift. While sti…
Minority Class Oriented Active Learning for Imbalanced Datasets
Umang Aggarwal, Adrian Popescu, Céline Hudelot
Active learning aims to optimize the dataset annotation process when resources are constrained. Most existing methods are designed for balanced datasets. Their practical applicabil…
A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
Umang Aggarwal, Adrian Popescu, Eden Belouadah +1
Deep learning approaches are successful in a wide range of AI problems and in particular for visual recognition tasks. However, there are still open problems among which is the cap…
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data. I…