Qualitative similarities and differences in visual object representations between brains and deep networks.

ABSTRACT: Deep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber's law, relative size, multiple object normalization and correlat

SUBMITTER: Jacob G 

PROVIDER: S-EPMC7994307 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature