Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial

Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial

Deep learning requires a large amount of datasets to train deep neural network models for specific tasks, and thus training of a new model is a very costly task. Research on transfer networks used to reduce training costs will be the next turning point in deep learning research. The use of source ta...

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Bibliographic Details
Main Authors: Feifei Lei, Jieren Cheng, Yue Yang, Xiangyan Tang, Victor S. Sheng, Chunzao Huang
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Electronics
Subjects:
deep learning
transfer learning
generative adversarial nets
heterogeneous network
Online Access:https://www.mdpi.com/2079-9292/10/13/1525

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https://www.mdpi.com/2079-9292/10/13/1525

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