基于Fisher正则化的自适应方差惩罚持续学习
Adaptive Variance-Penalized Continual Learning with Fisher Regularization
AI总结：
本文提出一种在变分学习框架中引入Fisher加权非对称方差正则化的持续学习方法，通过根据参数不确定性动态调节正则化强度，在多个标准基准上有效缓解灾难性遗忘并提升模型性能。
摘要：
神经网络中灾难性遗忘这一长期挑战推动了持续学习领域的广泛研究。本文提出了一种新颖的持续学习框架，在变分学习范式内整合了参数方差的Fisher加权非对称正则化。我们的方法根据参数不确定性动态调节正则化强度，从而实现更强的稳定性和性能。在包括SplitMNIST、PermutedMNIST和SplitFashionMNIST在内的标准持续学习基准上的综合评估表明，与Variational Continual Learning和Elastic Weight Consolidation等现有方法相比，我们的方法取得了显著改进。非对称方差惩罚机制在跨顺序任务保持知识方面被证明特别有效，同时提高了模型准确率。实验结果表明，我们的方法不仅提升了即时任务性能，还显著缓解了知识随时间的退化，有效应对了神经网络中灾难性遗忘这一根本性挑战。
英文摘要：
The persistent challenge of catastrophic forgetting in neural networks has motivated extensive research in continual learning . This work presents a novel continual learning framework that integrates Fisher-weighted asymmetric regularization of parameter variances within a variational learning paradigm. Our method dynamically modulates regularization intensity according to parameter uncertainty, achieving enhanced stability and performance. Comprehensive evaluations on standard continual learning benchmarks including SplitMNIST, PermutedMNIST, and SplitFashionMNIST demonstrate substantial improvements over existing approaches such as Variational Continual Learning and Elastic Weight Consolidation . The asymmetric variance penalty mechanism proves particularly effective in maintaining knowledge across sequential tasks while improving model accuracy. Experimental results show our approach not only boosts immediate task performance but also significantly mitigates knowledge degradation over time, effectively addressing the fundamental challenge of catastrophic forgetting in neural networks
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