New Optimization Framework Targets Forgetting in Continual Learning Models

Global: New Optimization Framework Targets Forgetting in Continual Learning Models

Breakthrough in Continual Learning

Researchers led by KaiHui Huang, along with RunQing Wu, JinHui Sheng, HanYi Zhang, Ling Ge, JinYu Guo, and Fei Ye, introduced an Optimally-Weighted Maximum Mean Discrepancy (OWMMD) framework to mitigate catastrophic forgetting in neural networks. The paper was first submitted to arXiv on 21 January 2025 and received its latest revision on 27 January 2026.

Addressing Catastrophic Forgetting

Continual learning aims to enable models to acquire new knowledge without erasing previously learned information, a challenge commonly referred to as catastrophic forgetting. The authors argue that existing regularization techniques often over-constrain model updates, limiting adaptability to future tasks.

Optimally-Weighted MMD Approach

The OWMMD framework imposes penalties on representation changes through a Multi-Level Feature Matching Mechanism (MLFMM), which aligns feature distributions across successive tasks at multiple network layers. By weighting these penalties optimally, the method seeks to preserve essential knowledge while allowing flexibility for new information.

Adaptive Regularization Optimization

Complementing OWMMD, the Adaptive Regularization Optimization (ARO) strategy dynamically adjusts weight vectors that quantify the importance of each feature layer during training. According to the authors, ARO alleviates over‑regularization and supports smoother integration of upcoming tasks.

Experimental Validation

The team evaluated the proposed approach against several established baselines on standard continual‑learning benchmarks. Reported results indicate that OWMMD combined with ARO achieved state‑of‑the‑art performance across the tested datasets, surpassing prior methods in both accuracy retention and forward transfer.

Open Access and Classification

The work is categorized under Machine Learning (cs.LG) and Artificial Intelligence (cs.AI) on arXiv and is available under an open‑access license. The full preprint can be accessed via the DOI https://doi.org/10.48550/arXiv.2501.12121. This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.