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- W4297584908 abstract "The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations. While simulations of these models recapitulate the ventral stream's progression from early view-specific to late view-tolerant representations, they fail to generate the most salient property of the intermediate representation for faces found in the brain: mirror-symmetric tuning of the neural population to head orientation. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules can provide approximate invariance at the top level of the network. While most of the learning rules do not yield mirror-symmetry in the mid-level representations, we characterize a specific biologically-plausible Hebb-type learning rule that is guaranteed to generate mirror-symmetric tuning to faces tuning at intermediate levels of the architecture." @default.
- W4297584908 created "2022-09-30" @default.
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- W4297584908 date "2016-06-05" @default.
- W4297584908 modified "2023-09-26" @default.
- W4297584908 title "View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation" @default.
- W4297584908 doi "https://doi.org/10.48550/arxiv.1606.01552" @default.
- W4297584908 hasPublicationYear "2016" @default.
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