Fig 4. Noise-trained VGG-19 outperforms human observers and other DNNs.

(a) Mean classification accuracy of noise-trained VGG-19 (blue), human observers (gray), and pretrained DNNs (red) for objects in pixelated Gaussian noise (solid lines, closed circles) and Fourier phase-scrambled noise (dashed lines, open circles). Noise-trained VGG-19 was trained with objects in Gaussian noise, Fourier phase-scrambled noise, and clean images from the 16 categories. (b) Frequency histograms comparing the SSNR thresholds of noise-trained VGG-19 (blue), individual human observers (gray), and 8 standard pretrained DNNs (red). Data are available at https://osf.io/bxr2v/. DNN, deep neural network; SSNR, signal-to-signal-plus-noise ratio.