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Model metamers illuminate divergences between biological and artificial neural networks

Jenelle Feather, Guillaume Leclerc, Aleksander Mądry, Josh H. McDermott
doi: https://doi.org/10.1101/2022.05.19.492678
Jenelle Feather
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
2McGovern Institute, Massachusetts Institute of Technology
3Center for Brains Minds and Machines, Massachusetts Institute of Technology
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Guillaume Leclerc
4Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
5Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
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Aleksander Mądry
4Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
5Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
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Josh H. McDermott
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
2McGovern Institute, Massachusetts Institute of Technology
3Center for Brains Minds and Machines, Massachusetts Institute of Technology
6Speech and Hearing Bioscience and Technology, Harvard University
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Abstract

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances we generated “model metamers” – stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from deep model stages, suggesting differences between model and human invariances. Targeted model changes improved human-recognizability of model metamers, but did not eliminate the overall human-model discrepancy. The human-recognizability of a model’s metamers was well predicted by their recognizability by other models, suggesting that models learn idiosyncratic invariances in addition to those required by the task. Metamer recognition dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 20, 2022.
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Model metamers illuminate divergences between biological and artificial neural networks
Jenelle Feather, Guillaume Leclerc, Aleksander Mądry, Josh H. McDermott
bioRxiv 2022.05.19.492678; doi: https://doi.org/10.1101/2022.05.19.492678
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Model metamers illuminate divergences between biological and artificial neural networks
Jenelle Feather, Guillaume Leclerc, Aleksander Mądry, Josh H. McDermott
bioRxiv 2022.05.19.492678; doi: https://doi.org/10.1101/2022.05.19.492678

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