Task-Free Continual Learning via Online Discrepancy Distance Learning

By Fei Ye, Adrian G. Bors. 2022

38 citations recorded by the source corpus.

Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information. Although recently some methods have been proposed for TFCL, they lack theoretical guarantees. Moreover, forgetting analysis during TFCL was not studied theoretically before. This paper develops a new theoretical analysis framework which provides generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model. This analysis gives new insights into the forgetting behaviour in classification tasks. Inspired by this theoretical model, we propose a new approach enabled by the dynamic component expansion mechanism for a mixture model, namely the Online Discrepancy Distance Learning (ODDL). ODDL estimates the discrepancy between the probabilistic representation of the current memory buffer and the already accumulated knowledge and uses it as the expansion signal to ensure a compact network architecture with optimal performance. We then propose a new sample selection approach that selectively stores the most relevant samples into the memory buffer through the discrepancy-based measure, further improving the performance. We perform several TFCL experiments with the proposed methodology, which demonstrate that the proposed approach achieves the state of the art performance.

Subfields: Artificial Intelligence, Meta & Continual Learning

How researchers see this paper

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

In Artificial Intelligence

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Hang Su
5. Qiwen Wang
6. Jun Zhu
7. Ziwei Liu
8. Liyuan Wang

In Meta & Continual Learning

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Xingxing Zhang
5. Liyuan Wang
6. Hang Su
7. Qiwen Wang
8. Jun Zhu

Citations in the corpus

• A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “TFCL CN-DPM [241], [187], VariGrow [19], ODDL [473]”
• 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).”
• 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].”
• Online Continual Learning For Interactive Instruction Following Agents (2024, methodology): “Approaches to continual learning can be categorized into task-free methods (Aljundi et al., 2019a; Koh et al., 2023; Ye & Bors, 2022; Koh et al., 2022) and task-aware methods (Kirkpatrick et al., 2017; Li & Hoiem, 2017; Wu et al., 2019; Boschini et al., 2022a) based on the use of task boundary…”