Deep neural networks (DNNs) can accurately recognize objects in clear viewing conditions, leading to claims that they have attained or surpassed human-level performance. However, standard DNNs are severely impaired at recognizing objects in visual noise, whereas human vision remains robust. We developed a noise-training procedure, generating noisy images of objects with low signal-to-noise ratio, to investigate whether DNNs can acquire robustness that better matches human vision. After noise training, DNNs outperformed human observers while exhibiting more similar patterns of performance, and provided a better model for predicting human recognition thresholds on an image-by-image basis. Noise training also improved DNN recognition of vehicles in noisy weather. Layer-specific analyses revealed that the contaminating effects of noise were dampened, rather than amplified, across successive stages of the noise-trained network, with greater benefit at higher levels of the network. Our findings indicate that DNNs can learn noise-robust representations that better approximate human visual processing.
bioRxiv Subject Collection: Neuroscience