Migrating Knowledge between Physical Scenarios Based on Artificial Neural Networks
© 2019 American Chemical Society. Deep learning is known to be data-hungry, which hinders its application in many areas of science when data sets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the predi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132373 |