Transfer Learning Algorithm With Knowledge Division Level

One of the major challenges of transfer learning algorithms is the domain drifting problem where the knowledge of source scene is inappropriate for the task of target scene. To solve this problem, a transfer learning algorithm with knowledge division level (KDTL) is proposed to subdivide knowledge o...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 34; číslo 11; s. 8602 - 8616
Hlavní autori: Han, Honggui, Liu, Hongxu, Yang, Cuili, Qiao, Junfei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Domain drifting problem
Elastic layers
hierarchal transfer learning algorithm
integrated learning method
Knowledge
Knowledge acquisition
Knowledge engineering
Knowledge management
Learning systems
Machine learning
Measurement
negative transfer
Prediction algorithms
Predictive models
Task analysis
Theoretical analysis
Transfer learning
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:One of the major challenges of transfer learning algorithms is the domain drifting problem where the knowledge of source scene is inappropriate for the task of target scene. To solve this problem, a transfer learning algorithm with knowledge division level (KDTL) is proposed to subdivide knowledge of source scene and leverage them with different drifting degrees. The main properties of KDTL are three folds. First, a comparative evaluation mechanism is developed to detect and subdivide the knowledge into three kinds-the ineffective knowledge, the usable knowledge, and the efficient knowledge. Then, the ineffective and usable knowledge can be found to avoid the negative transfer problem. Second, an integrated framework is designed to prune the ineffective knowledge in the elastic layer, reconstruct the usable knowledge in the refined layer, and learn the efficient knowledge in the leveraged layer. Then, the efficient knowledge can be acquired to improve the learning performance. Third, the theoretical analysis of the proposed KDTL is analyzed in different phases. Then, the convergence property, error bound, and computational complexity of KDTL are provided for the successful applications. Finally, the proposed KDTL is tested by several benchmark problems and some real problems. The experimental results demonstrate that this proposed KDTL can achieve significant improvement over some state-of-the-art algorithms.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3151646