%0 Generic %1 rahaman2018spectral %A Rahaman, Nasim %A Baratin, Aristide %A Arpit, Devansh %A Draxler, Felix %A Lin, Min %A Hamprecht, Fred A. %A Bengio, Yoshua %A Courville, Aaron %D 2018 %K ai math relu spectral %T On the Spectral Bias of Neural Networks %U http://arxiv.org/abs/1806.08734 %X Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.

@misc{rahaman2018spectral, abstract = {Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets \emph{easier} with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.}, added-at = {2021-02-03T09:54:02.000+0100}, author = {Rahaman, Nasim and Baratin, Aristide and Arpit, Devansh and Draxler, Felix and Lin, Min and Hamprecht, Fred A. and Bengio, Yoshua and Courville, Aaron}, biburl = {https://www.bibsonomy.org/bibtex/24297aafa6045daa9b53b66e9934d5f6d/louissf}, description = {On the Spectral Bias of Neural Networks}, interhash = {ed126edbcd89f7ae987f99aaf8d8133f}, intrahash = {4297aafa6045daa9b53b66e9934d5f6d}, keywords = {ai math relu spectral}, note = {cite arxiv:1806.08734Comment: 23 pages}, timestamp = {2021-02-03T09:56:10.000+0100}, title = {On the Spectral Bias of Neural Networks}, url = {http://arxiv.org/abs/1806.08734}, year = 2018 }