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Transfer Learning for Visual Categorization: A Survey
Open accessAbstract
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.
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“Transfer learning (TL) [70] is one of the model regularization techniques that have proven their effectiveness for training deep models with a limited amount of available data and avoiding overfitting problems.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [70]
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Metadata
- Authors
- Shao, L., Zhu, F., & Li, X.
- Affiliations
- Nanjing University of Information Science and Technology (CN)
- University of Sheffield (GB)
- Chinese Academy of Sciences (CN)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2015-05
- Volume
- 26
- Issue
- 5
- Pages
- 1019-1034
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2163345210
- Cited by
- 895
- References
- 97
- Citations by year
- 2026: 30 · 2025: 61 · 2024: 86 · 2023: 96 · 2022: 123 · 2021: 155 · 2020: 124 · 2019: 86 · 2018: 51 · 2017: 41 · 2016: 24 · 2015: 13
- FWCI
- 27.55
- Type
- journal article
- Language
- en
- Open access
- green
- ir.opt.ac.cn
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Institutional Repository of Xi'an Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Xian Institute of Optics and Precision Mechanics) (submittedVersion, cc-by)
- Northumbria Research Link (Northumbria University) (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- National Basic Research Program of China (973 Program) (2012CB316400)
- University of Sheffield, Sheffield, U.K.
- National Natural Science Foundation of China (61125106, 61072093)
- 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 › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- transfer learning, visual categorization, cross-domain learning, object recognition, image classification, human action recognition, concept drift, overfitting, domain adaptation
- MeSH terms
- Machine Learning, Algorithms, Humans, Models, Theoretical, Surveys and Questionnaires, Transfer, Psychology, Visual Perception, Neural Networks, Computer, Knowledge
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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