Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster Memory

Online Task-Free Continual Learning (OTFCL) aims to learn novel concepts from streaming data without accessing task information. Most memory-based approaches used in OTFCL are not suitable for unsupervised learning because they require accessing supervised signals to implement their sample selection...

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Vydané v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 26202 - 26212
Hlavní autori: Ye, Fei, Bors, Adrian G.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 16.06.2024
Predmet:
Computer vision
Continuing education
Diffusion model
Memory buffer
Memory management
Pattern recognition
Semantics
Source coding
Task-Free continual learning
Training
ISSN:1063-6919
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Shrnutí:Online Task-Free Continual Learning (OTFCL) aims to learn novel concepts from streaming data without accessing task information. Most memory-based approaches used in OTFCL are not suitable for unsupervised learning because they require accessing supervised signals to implement their sample selection mechanisms. In this study, we address this issue by proposing a novel memory management approach, namely the Dynamic Cluster Memory (DCM), which builds new memory clusters to capture distribution shifts over time without accessing any supervised signals. DCM introduces a novel memory expansion mechanism based on the knowledge discrepancy criterion, which evaluates the novelty of the incoming data as the signal for the memory expansion, ensuring a compact memory capacity. We also propose a new sample selection approach that automatically stores incoming data samples with similar semantic information in the same memory cluster, while also facilitating the knowledge diversity among memory clusters. Further-more, a novel memory pruning approach is proposed to automatically remove overlapping memory clusters through a graph relation evaluation, ensuring a fixed memory capacity while maintaining the diversity among the samples stored in the memory. The proposed DCM is model-free, plug-and-play, and can be used in both supervised and unsupervised learning without modifications. Empirical results on OTFCL experiments show that the proposed DCM outperforms the state-of-the-art while requiring fewer data samples to be stored. The source code is available at https://github.com/dtuzi123/DCM.
ISSN:1063-6919
DOI:10.1109/CVPR52733.2024.02476