November 24, 2020

Noise-robust recognition of objects by humans and deep neural networks

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

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