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English(EN) Fine-Grained Analysis of Catastrophic Forgetting in Continual Learning via Algorithmic Stability

新框架分析持续学习中的灾难性遗忘

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的算法稳定性框架来分析持续学习中的灾难性遗忘。该框架将遗忘分解为任务内和跨任务泛化差距以及经验遗忘。它为各种学习率调度和累积梯度对齐提供了理论界限，深入探讨了迭代优化如何影响先前学习任务的性能下降。在 Split CIFAR-100 和 Split MiniImageNet 上的实验验证了理论发现。 AI

影响 为持续学习中的一个核心挑战提供了理论见解，可能指导未来的模型开发。

排序理由 关于特定机器学习技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架分析持续学习中的灾难性遗忘

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关于特定机器学习技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guodong Zheng, Yan Sun, Peng Wang, Enneng Yang, Jiafei Lyu, Xiaochun Cao, Dacheng Tao, Li Shen ·

    Fine-Grained Analysis of Catastrophic Forgetting in Continual Learning via Algorithmic Stability

    arXiv:2610.31551v1 Announce Type: new Abstract: Understanding catastrophic forgetting, in which learning new tasks degrades performance on previously learned tasks, remains a fundamental challenge in continual learning (CL). Existing theoretical analyses often rely on specific mo…

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