图像识别的正则化学习

Xinjie Lan, K. Barner
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引用次数: 0

摘要

为了减少图像识别应用中的过拟合问题,提出了一种新的深度学习正则化学习算法。最重要的是,我们提出了一种新的概率表示来解释深度神经网络(dnn)的结构,它表明靠近输入的隐藏层形成了先验分布,因此dnn具有显式正则化,即先验分布。此外,我们表明反向传播学习算法是过度拟合的原因,因为它不能保证精确地学习先验分布。基于深度学习的理论解释,我们提出了一种新的深度神经网络正则化学习算法。与现有的大多数正则化方法通过降低dnn的训练复杂度来减少过拟合相比,本文方法通过更有效地训练相应的先验分布来减少过拟合,从而得到更强大的正则化。仿真在一个合成数据集上验证了所提出的概率表示,并在CIFAR-10数据集上验证了所提出的正则化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regularization Learning for Image Recognition
In order to reduce overfitting for the image recognition application, this paper proposes a novel regularization learning algorithm for deep learning. Above all, we propose a novel probabilistic representation for explaining the architecture of Deep Neural Networks (DNNs), which demonstrates that the hidden layers close to the input formulate prior distributions, thus DNNs have an explicit regularization, namely the prior distributions. Furthermore, we show that the backpropagation learning algorithm is the reason for overfitting because it cannot guarantee precisely learning the prior distribution. Based on the proposed theoretical explanation for deep learning, we propose a novel regularization learning algorithm for DNNs. In contrast to most existing regularization methods reducing overfitting by decreasing the training complexity of DNNs, the proposed method reduces overfitting through training the corresponding prior distribution in a more efficient way, thereby deriving a more powerful regularization. Simulations demonstrate the proposed probabilistic representation on a synthetic dataset and validate the proposed regularization on the CIFAR-10 dataset.
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