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Found 6 papers, 3 papers with code
Improving Plasticity in Online Continual Learning via Collaborative Learning
To this end, we propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts.
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agent has no prior knowledge of class boundaries or task change information.
Rethinking Momentum Knowledge Distillation in Online Continual Learning
While Knowledge Distillation (KD) has been extensively used in offline Continual Learning, it remains under-exploited in OCL, despite its potential.
New metrics for analyzing continual learners
This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones.
Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning
We propose to use the angular Gaussian distribution, which corresponds to a Gaussian projected on the unit-sphere and derive the associated loss function.
Contrastive Learning for Online Semi-Supervised General Continual Learning
We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data.
