Model metamers reveal divergent invariances between biological and artificial neural networks

TitleModel metamers reveal divergent invariances between biological and artificial neural networks
Publication TypeJournal Article
Year of Publication2023
AuthorsFeather, J, Leclerc, G, Mądry, A, McDermott, JH
JournalNature Neuroscience
Date Published10/2023
ISSN1097-6256
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 late 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 contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability 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.

URLhttps://www.nature.com/articles/s41593-023-01442-0
DOI10.1038/s41593-023-01442-0
Short TitleNat Neurosci

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