6卷 第5期
Electronic Information Science and Technology, Nanjing Agricultural University, Nanjing, Jiangsu, 211800, China
纸质出版日期：2025-05-19
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(2025). From full fine-tuning to parameter-efficient adaptation: a taxonomy of deep transfer learning strategies. 科技理论与实践（英文版）, 6(5), 118-122.
Zhang Wenkai.From full fine-tuning to parameter-efficient adaptation: a taxonomy of deep transfer learning strategies[J].Theory and Practice of Science and Technology,2025,06(05):118-122.
(2025). From full fine-tuning to parameter-efficient adaptation: a taxonomy of deep transfer learning strategies. 科技理论与实践（英文版）, 6(5), 118-122. DOI： 10.47297/taposatWSP2633-456923.20250605.
Zhang Wenkai.From full fine-tuning to parameter-efficient adaptation: a taxonomy of deep transfer learning strategies[J].Theory and Practice of Science and Technology,2025,06(05):118-122. DOI： 10.47297/taposatWSP2633-456923.20250605.
This paper focuses on the field of deep transfer learning
aiming to study the dual dilemma of traditional full-parameter fine-tuning in terms computing power demand and overfitting risk. By establishing a three-level taxonomy of parameter-efficient transfer learning
this paper analyzes in detail the methods such as parameter addition strategy parameter reparameterization
and parameter subset selection. It also refines the general principles such as minimal disturbance of feature space
task-knowledge decoupling
and incremental knowledge injection Simultaneously
it points out the current challenges of lack of theoretical interpretability (black-box nature) and weak cross-architecture generalization ability
and proposes future directions such as-explanation-based interpretable adaption and neural architecture search (NAS)-driven automatic adaption
aiming to construct a more systematic and efficient theoretical framework for deep transfer learning.
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