Improving Task-free Continual Learning by Distributionally Robust Memory Evolution

By Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao. 2022

54 citations recorded by the source corpus.

Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay approach could gradually become less effective for long data streams, as the model may memorize the stored examples and overfit the memory buffer. Second, existing methods overlook the high uncertainty in the memory data distribution since there is a big gap between the memory data distribution and the distribution of all the previous data examples. To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO). We then derive a family of methods to evolve the memory buffer data in the continuous probability measure space with Wasserstein gradient flow (WGF). The proposed DRO is w.r.t the worst-case evolved memory data distribution, thus guarantees the model performance and learns significantly more robust features than existing memory-replay-based methods. Extensive experiments on existing benchmarks demonstrate the effectiveness of the proposed methods for alleviating forgetting. As a by-product of the proposed framework, our method is more robust to adversarial examples than existing task-free CL methods. Code is available on GitHub \url{https://github.com/joey-wang123/DRO-Task-free}

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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 Computer Vision and Pattern Recognition

1. Han-Jia Ye
2. De-chuan Zhan
3. Jun Zhu
4. Da-Wei Zhou
5. Hang Su
6. Ziwei Liu
7. Liyuan Wang
8. Qiwen 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): “Meanwhile, the memory buffer can be dynamically managed, such as by removing less important training samples (CLIB [224]), editing the old training samples (GMED [191]) or their distributions (DRO [443]) to be more likely forgotten, and retrieving the old training samples that are susceptible to int”
• The Ideal Continual Learner: An Agent That Never Forgets (2023, methodology): “…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).”
• Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (2022, background): “Many works mitigate catastrophic forgetting during the learning process [37,14,48,68,31,42,20,2,10,3,64].”
• 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].”