Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

27 Aug 2018 Yurui Qu Li Jing Yichen Shen Min Qiu Marin Soljacic

Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset... (read more)

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