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Cited by 314Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsReinforcement Learning in Robotics

Transfer adaptation learning: A decade survey

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Zhang, Lei, Gao, Xinbo·2024·IEEE Transactions on Neural Networks and Learning Systems
DOI 10.1109/tnnls.2022.3183326
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Abstract

The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic-related but distribution different source domain. It is an energetic research field of increasing influence and importance, which is presenting a blowout publication trend. This article surveys the advances of TAL methodologies in the past decade, and the technical challenges and essential problems of TAL have been observed and discussed with deep insights and new perspectives. Broader solutions of TAL being created by researchers are identified, i.e., instance reweighting adaptation, feature adaptation, classifier adaptation, deep network adaptation, and adversarial adaptation, which are beyond the early semisupervised and unsupervised split. The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges, and understudied issues (universality, interpretability, and credibility) to be broken in the field toward generalizable representation in open-world scenarios.

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  • Prior research on domain adaptation and domain generalization highlights the cross-scenario generalization issue but often lacks comprehensive, practical strategies tailored specifically for deepfake detection.

    “Prior research on domain adaptation [258] and domain generalization [259] highlights this issue but often lacks comprehensive, practical strategies tailored specifically for deepfake detection.”

    From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [258]

    The Introduction (page unavailable) identifies domain shift as a challenge to applying classifiers across domains, supporting the cross-scenario generalization point. The survey does not assess deepfake detection or establish that prior research often lacks comprehensive, practical strategies tailored to it; a search for deepfake and forgery detection found no relevant passage. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Zhang, L., & Gao, X.
Affiliations
  • Chongqing University (CN)
  • Chongqing University of Posts and Telecommunications (CN)
ORCID
Venue
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024-01
Volume
35
Issue
1
Pages
23-44
ISSN
  • 2162-237X (ISSN-L)
  • 2162-2388
DOI
10.1109/tnnls.2022.3183326
OpenAlex
W2935033494
Cited by
314
References
376
Citations by year
2026: 34 · 2025: 64 · 2024: 52 · 2023: 62 · 2022: 27 · 2021: 39 · 2020: 29 · 2019: 7
FWCI
16.87
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Chongqing Natural Science Fund (cstc2021jcyj-jqX0023)
  • National Key Research and Development Program of China (2021YFB3100800)
  • CCF Hikvision Open Fund (CCF-HIKVISION OF 20210002)
  • CAAI-Huawei MindSpore Open Fund
  • Beijing Academy of Artificial Intelligence
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
domain adaptation, cross-domain knowledge transfer, feature adaptation, classifier adaptation, adversarial adaptation, open-world learning, interpretability

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