Continual Learning by Modeling Intra-Class Variation

By L. Yu, Tianyang Hu, Lanqing Hong, Zhen Liu, Adrian Weller, Weiyang Liu. 2022

19 citations recorded by the source corpus.

It has been observed that neural networks perform poorly when the data or tasks are presented sequentially. Unlike humans, neural networks suffer greatly from catastrophic forgetting, making it impossible to perform life-long learning. To address this issue, memory-based continual learning has been actively studied and stands out as one of the best-performing methods. We examine memory-based continual learning and identify that large variation in the representation space is crucial for avoiding catastrophic forgetting. Motivated by this, we propose to diversify representations by using two types of perturbations: model-agnostic variation (i.e., the variation is generated without the knowledge of the learned neural network) and model-based variation (i.e., the variation is conditioned on the learned neural network). We demonstrate that enlarging representational variation serves as a general principle to improve continual learning. Finally, we perform empirical studies which demonstrate that our method, as a simple plug-and-play component, can consistently improve a number of memory-based continual learning methods by a large margin.

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. Liyuan Wang
7. Ziwei Liu
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, methodology): “MOCA [482] enlarges the variation of representations to prevent the old ones from shrinking in their space.”
• Class-Incremental Learning: A Survey (2023, background): “…on a small pool of data inevitably leads to tight and unstable decision boundaries, several works tackle this problem by constraining the model’s layer-wise Lipschitz constants with regard to exemplars [163] or enlarging representational variations to alleviate representation collapse [164].”