An Information Theoretic Interpretation to Deep Neural Networks
With the unprecedented performance achieved by deep learning, it is commonly believed that deep neural networks (DNNs) attempt to extract informative features for learning tasks. To formalize this intuition, we apply the local information geometric analysis and establish an information-theoretic fra...
| Main Authors: | , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/139647 |
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An Information Theoretic Interpretation to Deep Neural Networks
Published 2021
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