English(EN) The Accuracy Well: Nonmonotone Accuracy Dynamics at Vanishing Initialization

新研究揭示深度学习动态中的“精度之井”现象

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员在深度学习模型中发现了一种称为“精度之井”的现象，即精度先增加，然后下降，最后恢复。这种非单调行为已在各种网络类型和数据集（包括 MNIST 和 CIFAR-10）以及不同的损失函数和优化器中观察到。该研究提出了一个涉及目标驱动组织、退化瓶颈和后续招募的三阶段解释，为线性网络中的这些动态提供了一个理论框架，并展示了精度在稳定之前大幅波动的极端情况。 AI

影响揭示了模型训练中一种违反直觉的行为，可能需要新的优化策略。

排序理由学术论文，详细介绍了机器学习动态中的一种新颖现象。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yaroslav Gusev, Eugene Golikov, Dmitry Yarotsky · 2026-10-15 04:00

精确井：消失初始化下的非单调精度动态

arXiv:2610.20439v1 Announce Type: new Abstract: We study classification dynamics under small initialization and identify a pronounced non-monotone behavior of accuracy that we call the accuracy well: accuracy rises rapidly early in training, subsequently drops, and only much late…

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精确井：消失初始化下的非单调精度动态

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