Robust deep learning object recognition models rely on low frequency information in natural images
Fig 3
Probing frequency sensitivity of mouse regularized model using hybrid images.
(a) Examples of hybrid images at different mixing frequencies. Hybrid images are constructed by mixing the low-frequency component of one image and the high-frequency component of another, while the two seed images belong to different categories. The range of mixing frequency’s values are normalized by the Nyquist frequency. (b) Model predictions on hybrid images at different mixing frequencies. As more low-frequencies from one image are included, the probability that a network reports its label plow increases. The reversal frequency frev where plow = phigh is smaller for the mouse regularized model (‘neural’) than for the baseline model (‘base’).