Real-time evaluation in online continual learning: A new hope
Y Ghunaim, A Bibi, K Alhamoud… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract Current evaluations of Continual Learning (CL) methods typically assume that there
is no constraint on training time and computation. This is an unrealistic assumption for any …
is no constraint on training time and computation. This is an unrealistic assumption for any …
Computationally budgeted continual learning: What does matter?
A Prabhu, HA Al Kader Hammoud… - Proceedings of the …, 2023 - openaccess.thecvf.com
Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …
vary in distribution by preserving previous knowledge while adapting to new data. Current …
Online continual learning with natural distribution shifts: An empirical study with visual data
Z Cai, O Sener, V Koltun - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Continual learning is the problem of learning and retaining knowledge through time over
multiple tasks and environments. Research has primarily focused on the incremental …
multiple tasks and environments. Research has primarily focused on the incremental …
Avalanche: an end-to-end library for continual learning
V Lomonaco, L Pellegrini, A Cossu… - Proceedings of the …, 2021 - openaccess.thecvf.com
Learning continually from non-stationary data streams is a long-standing goal and a
challenging problem in machine learning. Recently, we have witnessed a renewed and fast …
challenging problem in machine learning. Recently, we have witnessed a renewed and fast …
Online continual learning under extreme memory constraints
E Fini, S Lathuiliere, E Sangineto, M Nabi… - Computer Vision–ECCV …, 2020 - Springer
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially
learn new tasks while being able to retain knowledge obtained from past experiences. In this …
learn new tasks while being able to retain knowledge obtained from past experiences. In this …
Gcr: Gradient coreset based replay buffer selection for continual learning
R Tiwari, K Killamsetty, R Iyer… - Proceedings of the …, 2022 - openaccess.thecvf.com
Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …
increasing number of tasks encountered sequentially, thereby potentially leveraging …
Regularizing second-order influences for continual learning
Z Sun, Y Mu, G Hua - … of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
Continual learning aims to learn on non-stationary data streams without catastrophically
forgetting previous knowledge. Prevalent replay-based methods address this challenge by …
forgetting previous knowledge. Prevalent replay-based methods address this challenge by …
Online continual learning on a contaminated data stream with blurry task boundaries
J Bang, H Koh, S Park, H Song… - Proceedings of the …, 2022 - openaccess.thecvf.com
Learning under a continuously changing data distribution with incorrect labels is a desirable
real-world problem yet challenging. Large body of continual learning (CL) methods …
real-world problem yet challenging. Large body of continual learning (CL) methods …
A comprehensive empirical evaluation on online continual learning
A Soutif-Cormerais, A Carta, A Cossu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …
on a stream of data with temporally shifting distribution and by storing a minimum amount of …
Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning
L Wang, K Yang, C Li, L Hong… - Proceedings of the …, 2021 - openaccess.thecvf.com
Continual learning usually assumes the incoming data are fully labeled, which might not be
applicable in real applications. In this work, we consider semi-supervised continual learning …
applicable in real applications. In this work, we consider semi-supervised continual learning …