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Volume 28, Issue 5
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao & Zheng Ma

DOI: 10.4208/cicp.OA-2020-0085

Commun. Comput. Phys., 28 (2020), pp. 1746-1767.

Published online: 2020-11

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  • Abstract

We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) — DNNs often fit target functions from low to high frequencies — on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural networks such as VGG16. This F-Principle of DNNs is opposite to the behavior of Jacobi method, a conventional iterative numerical scheme, which exhibits faster convergence for higher frequencies for various scientific computing problems. With theories under an idealized setting, we illustrate that this F-Principle results from the smoothness/regularity of the commonly used activation functions. The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function. This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or a randomized dataset.

  • Keywords

Deep learning, training behavior, generalization, Jacobi iteration, Fourier analysis.

  • AMS Subject Headings

68Q32, 65N06, 68T01

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-28-1746, author = {John Xu , Zhi-QinZhang , YaoyuLuo , TaoXiao , Yanyang and Ma , Zheng}, title = {Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks}, journal = {Communications in Computational Physics}, year = {2020}, volume = {28}, number = {5}, pages = {1746--1767}, abstract = {

We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) — DNNs often fit target functions from low to high frequencies — on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural networks such as VGG16. This F-Principle of DNNs is opposite to the behavior of Jacobi method, a conventional iterative numerical scheme, which exhibits faster convergence for higher frequencies for various scientific computing problems. With theories under an idealized setting, we illustrate that this F-Principle results from the smoothness/regularity of the commonly used activation functions. The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function. This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or a randomized dataset.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2020-0085}, url = {http://global-sci.org/intro/article_detail/cicp/18395.html} }
TY - JOUR T1 - Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks AU - John Xu , Zhi-Qin AU - Zhang , Yaoyu AU - Luo , Tao AU - Xiao , Yanyang AU - Ma , Zheng JO - Communications in Computational Physics VL - 5 SP - 1746 EP - 1767 PY - 2020 DA - 2020/11 SN - 28 DO - http://doi.org/10.4208/cicp.OA-2020-0085 UR - https://global-sci.org/intro/article_detail/cicp/18395.html KW - Deep learning, training behavior, generalization, Jacobi iteration, Fourier analysis. AB -

We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) — DNNs often fit target functions from low to high frequencies — on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural networks such as VGG16. This F-Principle of DNNs is opposite to the behavior of Jacobi method, a conventional iterative numerical scheme, which exhibits faster convergence for higher frequencies for various scientific computing problems. With theories under an idealized setting, we illustrate that this F-Principle results from the smoothness/regularity of the commonly used activation functions. The F-Principle implies an implicit bias that DNNs tend to fit training data by a low-frequency function. This understanding provides an explanation of good generalization of DNNs on most real datasets and bad generalization of DNNs on parity function or a randomized dataset.

John Xu , Zhi-QinZhang , YaoyuLuo , TaoXiao , Yanyang and Ma , Zheng. (2020). Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks. Communications in Computational Physics. 28 (5). 1746-1767. doi:10.4208/cicp.OA-2020-0085
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