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A novel deep transfer learning method based on explainable feature extraction and domain reconstruction.
Li Wang1, Lucong Zhang1, Ling Feng1
1College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Summary
This study introduces XDTL, a novel multi-stage deep transfer learning method. XDTL enhances model performance and explainability by combining feature extraction and domain reconstruction, achieving significant effectiveness improvements.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Deep transfer learning faces challenges with "black-box" models and unstable feature adaptation.
- Explainability and reliable feature adaptation are crucial for advancing transfer learning.
Purpose of the Study:
- To propose a multi-stage deep transfer learning method (XDTL) that enhances model performance and explainability.
- To address the limitations of current deep transfer learning techniques through explainable feature extraction and domain reconstruction.
Main Methods:
- XDTL divides features into key and regular types using cross-validation and explainability analysis.
- It employs a seed replacement strategy with key target samples for target domain reconstruction.
- This approach facilitates a deep transfer process.
Main Results:
- XDTL demonstrated an average effectiveness improvement of 27.43% compared to existing methods.
- The proposed method shows superior performance and enhanced explainability in transfer learning tasks.
Conclusions:
- XDTL offers a promising solution for the explainability challenges in deep transfer learning.
- The method provides new insights and potential for diverse applications across various machine learning tasks.
