Separability and geometry of object manifolds in deep neural networks
Neural activity space or manifold that represents object information changes across the layers of a deep neural network. Here the authors present a theoretical account of the relationship between the geometry of the manifolds and the classification capacity of the neural networks.
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-14578-5 |