动态调节参数不确定性，缓解神经网络持续学习中的灾难性遗忘。
Adaptive Variance-Penalized Continual Learning with Fisher Regularization
- 基于变分学习框架，用费雪信息动态调节参数方差惩罚强度。
- 在SplitMNIST等基准上显著优于VCL和EWC等方法，准确率提升明显。
- 特别适合需要长期稳定记忆的持续学习场景，如智能助手、自动驾驶。
神经网络中的灾难性遗忘问题长期困扰持续学习研究。本文提出一种新框架，将费雪加权的非对称参数方差正则化融入变分学习范式。该方法根据参数不确定性动态调节正则化强度，提升了模型稳定性和性能。在SplitMNIST、PermutedMNIST和SplitFashionMNIST等标准基准上的综合评估显示，其表现显著优于变分持续学习（VCL）和弹性权重整合（EWC）等现有方法。非对称方差惩罚机制在保持多任务知识的同时提升了模型准确率。实验表明，该方法不仅增强了当前任务表现，还显著缓解了随时间推移的知识退化，有效应对了神经网络持续学习中的根本挑战。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。