12 citations · 18 across the 7 of their papers we have counts for
7 papers
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…
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
Grégoire Petit, Adrian Popescu, Hugo Schindler +2
Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental pr…
PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning
Grégoire Petit, Adrian Popescu, Eden Belouadah +2
Plasticity and stability are needed in class-incremental learning in order to learn from new data while preserving past knowledge. Due to catastrophic forgetting, finding a comprom…
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…
Optimizing Active Learning for Low Annotation Budgets
Umang Aggarwal, Adrian Popescu, Céline Hudelot
When we can not assume a large amount of annotated data , active learning is a good strategy. It consists in learning a model on a small amount of annotated data (annotation budget…
Face Verification with Challenging Imposters and Diversified Demographics
Adrian Popescu, Liviu-Daniel Ştefan, Jérôme Deshayes-Chossart +1
Face verification aims to distinguish between genuine and imposter pairs of faces, which include the same or different identities, respectively. The performance reported in recent…
Dataset Knowledge Transfer for Class-Incremental Learning without Memory
Habib Slim, Eden Belouadah, Adrian Popescu +1
Incremental learning enables artificial agents to learn from sequential data. While important progress was made by exploiting deep neural networks, incremental learning remains ver…