The DOI matches its Crossref record; 96 of the 97 references it cites were found in a registry or an index.
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]
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Wold, S., Esbensen, K. H., & Geladi, P. L. M. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,792
Recognition-by-components: A theory of human image understanding.
Biederman, I. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.115 · Cited by 5,621
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481
Research works printed but not linked: 5
- lanckriet, 2004, Learning the kernel matrix with semidefinite programming, J Mach Learn Res, 5, 27
- rakotomamonjy, 2008, SimpleMKL, J Mach Learn Res, 9, 2491
- fei-fei, 2006, Knowledge transfer in learning to recognize visual objects classes, Proc 5th Int Conf Develop Learn
- 2007, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
- olivas, 2009, Handbook Of Research On Machine Learning Applications and Trends Algorithms Methods and Techniques, 2
Cited by
Sorted by citations
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Brain tumor classification using deep CNN features via transfer learning
Deepak, S., & Ameer, P. M. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103345 · Cited by 1,370
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
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