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Found 4 papers, 3 papers with code
Learning Equi-angular Representations for Online Continual Learning
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e. g., single-epoch training).
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
A large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplored.
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference
For better practicality, we first propose a novel continual learning setup that is online, task-free, class-incremental, of blurry task boundaries and subject to inference queries at any moment.
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels
Specifically, we argue the importance of both diversity and purity of examples in the episodic memory of continual learning models.
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