向批量归一化中的正则化理解迈进
Towards Understanding Regularization in Batch Normalization
AI总结：
本文通过理论分析探讨了批量归一化在神经网络训练中的收敛性和泛化能力，揭示了批量归一化作为隐式正则化的作用，并通过实验验证了其在卷积神经网络中的正则化特性。
摘要：
批量归一化（BN）在神经网络训练中提高了收敛性和泛化能力。本工作从理论上理解这些现象。我们通过使用由核层、BN层和非线性激活函数组成的基本网络块来分析BN。这个基本网络帮助我们从三个方面理解BN的影响。首先，将BN视为隐式正则化，可以将其分解为总体归一化（PN）和伽马衰减作为显式正则化。其次，BN和正则化的学习动态表明，使用大最大有效学习率训练可以收敛。第三，通过统计力学探讨BN的泛化能力。实验表明，卷积神经网络中的BN共享上述分析中的正则化特性。
英文摘要：
Batch Normalization (BN) improves both convergence and generalization in training neural networks. This work understands these phenomena theoretically. We analyze BN by using a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function. This basic network helps us understand the impacts of BN in three aspects. First, by viewing BN as an implicit regularizer, BN can be decomposed into population normalization (PN) and gamma decay as an explicit regularization. Second, learning dynamics of BN and the regularization show that training converged with large maximum and effective learning rate. Third, generalization of BN is explored by using statistical mechanics. Experiments demonstrate that BN in convolutional neural networks share the same traits of regularization as the above analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
