Branch scaling improves ResNet generalization via depth-wise decay

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员从理论上证明，通过快速随深度衰减的缩放因子，可以提高宽残差网络（ResNets）的泛化能力。这种方法与提前停止相结合，可以使过参数化的ResNets达到最佳泛化率。该研究通过在合成数据和MNIST、CIFAR-100等常见分类任务上的实验验证了这些发现，为增强网络性能提供了新的途径。 AI

影响为提高过参数化神经网络的泛化能力提供了理论见解，可能指导未来的模型架构。

排序理由详细介绍神经网络泛化理论发现和实验验证的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zixiong Yu, Guhan Chen, Jianfa Lai, Bohan Li, Songtao Tian · 2026-05-26 04:00

分支缩放表现为隐式架构正则化，以提高过参数化ResNets的泛化能力

arXiv:2403.04545v3 Announce Type: replace Abstract: Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free architectures. While prior work has primarily examined scaling effects from an opt…