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Despite their ability to represent highly expressive functions, deep learningmodels seem to find simple solutions that generalize surprisingly well.Spectral bias -- the tendency of neural networks to prioritize learning lowfrequency functions -- is one possible explanation for this phenomenon, but sofar spectral bias has primarily been observed in theoretical models andsimplified experiments. In this work, we propose methodologies for measuringspectral bias in modern image classification networks on CIFAR-10 and ImageNet.We find that these networks indeed exhibit spectral bias, and thatinterventions that improve test accuracy on CIFAR-10 tend to produce learnedfunctions that have higher frequencies overall but lower frequencies in thevicinity of examples from each class. This trend holds across variation intraining time, model architecture, number of training examples, dataaugmentation, and self-distillation. We also explore the connections betweenfunction frequency and image frequency and find that spectral bias is sensitiveto the low frequencies prevalent in natural images. On ImageNet, we find thatlearned function frequency also varies with internal class diversity, withhigher frequencies on more diverse classes. Our work enables measuring andultimately influencing the spectral behavior of neural networks used for imageclassification, and is a step towards understanding why deep models generalizewell.

Spectral Bias in Practice: The Role of Function Frequency in Generalization