Transfer Learning for Visual Categorization: A Survey

Ling Shao; Fan Zhu; Xuelong Li

doi:10.1109/tnnls.2014.2330900

Cited by 895Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Transfer Learning for Visual Categorization: A Survey

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Ling Shao, Fan Zhu, Xuelong Li·2015·IEEE Transactions on Neural Networks and Learning Systems

DOI 10.1109/tnnls.2014.2330900

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Abstract

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 is a model regularization technique proven effective for training deep models with limited data and avoiding overfitting.
“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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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)
ORCID: Ling Shao
Fan Zhu
Xuelong Li
Venue: IEEE Transactions on Neural Networks and Learning Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2015-05
Volume: 26
Issue: 5
Pages: 1019-1034
ISSN: 2162-237X (ISSN-L)
2162-2388
DOI: 10.1109/tnnls.2014.2330900
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 · 2014: 5
FWCI: 27.55
Type: journal article
Language: en
Open access: green
ir.opt.ac.cn
Licence: cc-by
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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

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Transfer Learning for Visual Categorization: A Survey

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