Online Task-free Continual Learning with Dynamic Sparse Distributed Memory

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “GCL GSS [17], MIR [14], CLIB [224], GMED [191], DRO [443], GDumb [340], DER [46], GEM [277], A-GEM [65], DSDM [338], CoPE [86], BLD [117]”
The Ideal Continual Learner: An Agent That Never Forgets (2023, background): “…and more challenging case of task-agnostic or task-free continual learning, where in the training phase the learner has no access to task identities (Zeno et al., 2018; 2021; Aljundi et al., 2019a; Lee et al., 2020; Jin et al., 2021; Wang et al., 2022c; Pourcel et al., 2022; Ye & Bors, 2022).”
Class-Incremental Learning: A Survey (2023, methodology): “…Current CIL methods have a set of restrictions on the data stream, e.g. , saving exemplars for rehearsal, undergoing multi-epoch offline training within an incremental task, etc. Future CIL algorithms should be able to conduct fully online training [221] without requiring the task boundaries [222].”
Deep Class-Incremental Learning: A Survey (2023, background): “Future class-incremental learning algorithms should be able to conduct fully online training [226], [227], [228], [229], [230] without requiring the task boundaries [231], [232], [233].”