Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images.

ABSTRACT: Deep neural networks (DNNs) for object classification have been argued to provide the most promising model of the visual system, accompanied by claims that they have attained or even surpassed human-level performance. Here, we evaluated whether DNNs provide a viable model of human vision when tested with challenging noisy images of objects, sometimes presented at the very limits of visibility. We show that popular state-of-the-art DNNs perform in a qualitatively different manner than humans-they are unusually susceptible to spatially uncorrelated white noise and less impaired by spatially correlated noise. We implemented a noise training procedure to determine whether noise-trained DNNs exhibit more robust responses that better match human behavioral and neural performance. We found that n

SUBMITTER: Jang H 

PROVIDER: S-EPMC8659651 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature