Lifelong Representation Learning

DESCRIPTION

Given a set of problems to solve, the dominant paradigm in the AI community has been to solve each problem or task independently. This is in sharp contrast with the human capability to build from past experience and transfer knowledge to speed-up the learning process for a new task. To mimic such a capability, the machine learning community has introduced the concept of continual learning or lifelong learning. The main advantage of this paradigm is that it enables learning with less data, it often allows to learn faster and to generalize better. From an industrial standpoint, the potential of lifelong learning is tremendous as this would mean deploying machine learning models faster by bypassing the need to collect labels.

ACTIVITIES

Our chair is structured around several lines of research including self-supervised learning, continual adaptation, online learning, and stream learning.

We collaborate with the chair lead by Julien Mairal, entitled Towards More Data Efficiency in Machine Learning.

CHAIR EVENTS

Wiki-M3L workshop at ICLR22 (April 2022)
ImageNet Past, Present, Future workshop at NeurIPS21 (December 2021)
Invited talk at the ANITI research institute: “Lifelong visual representation learning: learning from weak supervision & mitigating catastrophic forgetting (May 2021)
Invited talk at the Women in Computer Vision (WiCV) Workshop at CVPR21 (August 2021)
PAISS summer school, involving 300+ participants from all over the world (July 2021)
Keynote at the Instance-Level Recognition Workshop at ECCV20: "From Instance-Level to Semantic Image Retrieval" (August 2020)

SELECTED LIST OF PUBLICATIONS

On the Road to Online Adaptation for Semantic Image Segmentation. R. Volpi, P. De Jorge, D. Larlus, G. Csurka. Computer Vision and Pattern Recognition conference (CVPR), 2022.
Concept Generalization in Visual Representation Learning. M. B. Sariyildiz, Y. Kalantidis, D. Larlus, K. Alahari. (ICCV), 2021.
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning. R. Volpi, D. Larlus, G. Rogez. Computer Vision and Pattern Recognition conference (CVPR), 2021.
Hard Negative Mixing for Contrastive Learning. Y. Kalantidis, M. B. Sariyildiz, N. Pion, P. Weinzaepfel, D. Larlus. (NeurIPS) 2020.
Learning Visual Representations with Caption Annotations. B. Sariyildiz, J. Perez, D. Larlus. European Conference on Computer Vision (ECCV) 2020.