October 31, 2020

The development of transformation tolerant visual representations differs between the human brain and convolutional neural networks

Existing single cell neural recording findings predict that, as information ascends the visual processing hierarchy in the primate brain, the relative similarity among the objects would be increasingly preserved across identity-preserving image transformations. Here we confirm this prediction and show that object category representational structure becomes increasingly invariant across position and size changes as information ascends the human ventral visual processing pathway. Such a representation, however, is not found in 14 different convolutional neural networks (CNNs) trained for object categorization that varied in architecture, depth and the presence/absence of recurrent processing. CNNs thus do not appear to form or maintain brain-like transformation-tolerant object identity representations at higher levels of visual processing despite the fact that CNNs may classify objects under various transformations. This limitation could potentially contribute to the large number of training data required to train CNNs and their limited ability to generalize to objects not included in training.

 bioRxiv Subject Collection: Neuroscience

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