Research on continual learning method for overcoming catastrophic forgetting of artificial neural networks

doi:10.12267/j.issn.2096-5931.2026.01.010

Abstract

Abstract:

Traditional artificial neural network training typically focuses on closed, static, independent and identically distributed data, and performs a single task after completing offline training. However, when the data distribution continuously changes with the environment, the model will forget the knowledge learned from previous tasks, a phenomenon known as “catastrophic forgetting”. As an emerging learning paradigm, continual learning aims to endow models with the ability to continuously learn, accumulate, and consolidate knowledge from data streams with constantly changing distributions. This enables artificial neural networks to achieve a “stability-plasticity” balance, thereby overcoming catastrophic forgetting. Through in-depth analysis of the key characteristics of current continual learning algorithms, a real-world robotic physical verification platform was established. The effectiveness of continual learning algorithms was verified in the scenario of robotic physical object grasping. Experimental results show that when the Contrastive Correlation Preserving Replay (CCPR) algorithm is applied to the robotic physical object grasping task, the average accuracy of the grasping task increases by 26.67%, better assisting the robot in performing the target task.

Key words: artificial intelligence, artificial neural networks, continual learning, catastrophic forgetting

CLC Number:

TP183

YU Da, DONG Xiaofei, CAO Feng, ZHA Fusheng, SUN Lining. Research on continual learning method for overcoming catastrophic forgetting of artificial neural networks[J]. Information and Communications Technology and Policy, 2026, 52(1): 75-83.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2026.01.010

http://ictp.caict.ac.cn/EN/Y2026/V52/I1/75

Figures/Tables 7

References 15

[1]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// Advances in Neural Information Processing Systems 25 (NIPS 2012). Lake Tahoe: NeurIPS, 2012: 1106-1114.
[2]	MCCLOSKEY M, COHEN N J. Catastrophic interference in connectionist networks: the sequential learning problem[J]. Psychology of Learning and Motivation. 1989, 24: 109-165.
[3]	ALJUNDI R, KELCHTERMANS K, TUYTELAARS T. Task-Free continual learning[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Leuven: IEEE Press, 2019: 11254-11263.
[4]	KIRKPATRICK J, PASCANU R, RABINOWITZ N, et al. Overcoming catastrophic forgetting in neural networks[J]. Proceedings of the National Academy of Sciences, 2017, 114(13): 3521-3526. doi: 10.1073/pnas.1611835114 URL
[5]	ZENKE F, POOLE B, GANGULI S. Continual learning through synaptic intelligence[C]// International Conference on Machine Learning. Sydney: ICML, 2017: 3987-3995.
[6]	LI Z, HOIEM D. Learning without forgetting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(12): 2935-2947. doi: 10.1109/TPAMI.34 URL
[7]	RANNEN A, ALJUNDI R, BLASCHKO M B, et al. Encoder based lifelong learning[C]// Proceedings of the IEEE International Conference on Computer Vision. Venice: ICCV, 2017: 1320-1328.
[8]	RUSU A A, RABINOWITZ N C, DESJARDINS G, et al. Progressive neural networks[J]. arXiv Preprint, arXiv:1606.04671, 2016.
[9]	YOON J, YANG E, LEE J, et al. Lifelong learning with dynamically expandable networks[C]. International Conference on Learning Representations. Vancouver: ICLR, 2018.
[10]	FERNANDO C, BANARSE D, BLUNDELL C, et al. Pathnet: evolution channels gradient descent in super neural networks[J]. arXiv Preprint, arXiv:1701.08734, 2017.
[11]	MALLYA A, LAZEBNIK S. PackNet: adding multiple tasks to a single network by iterative pruning[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE Press, 2018: 7765-7773.
[12]	RIEMER M, CASES I, AJEMIAN R, et al. Learning to learn without forgetting by maximizing transfer and minimizing interference[C]// International Conference on Learning Representations. New Orleans: ICLR, 2019.
[13]	YU D, ZHANG M, LI M, et al. Contrastive correlation preserving replay for online continual learning[J]. IEEE Transactions on Circuits and Systems for Video Technology. 2023, 34 (1): 124-139. doi: 10.1109/TCSVT.2023.3285221 URL
[14]	SHIN H, LEE J K, KIM J, et al. Continual learning with deep generative replay[J]. Advances in Neural Information Processing Systems, 2017: 2990-2999.
[15]	LOPEZ-PAZ D, RANZATO M. Gradient episodic memory for continual learning[J]. Advances in Neural Information Processing Systems, 2017: 6467-6476.