83 citations · 194 across the 28 of their papers we have counts for
8 papers · 1 filter
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Alexandre Lacoste, Evan David Sherwin, Hannah Kerner +9
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downst…
Multi-label Iterated Learning for Image Classification with Label Ambiguity
Sai Rajeswar, Pau Rodriguez, Soumye Singhal +2
Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled…
Continual Learning via Local Module Composition
Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia +1
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then composing modules to solve different tasks pro…
A Survey of Self-Supervised and Few-Shot Object Detection
Gabriel Huang, Issam Laradji, David Vazquez +2
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-s…
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko +10
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a…
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma +4
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on pa…