The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
A Survey of Transfer Learning for Convolutional Neural Networks
Abstract
Transfer learning is an emerging topic that may drive the success of machine learning in research and industry. The lack of data on specific tasks is one of the main reasons to use it, since collecting and labeling data can be very expensive and can take time, and recent concerns with privacy make difficult to use real data from users. The use of transfer learning helps to fast prototype new machine learning models using pre-trained models from a source task since training on millions of images can take time and requires expensive GPUs. In this survey, we review the concepts and definitions related to transfer learning and we list the different terms used in the literature. We bring the point of view from different authors of prior surveys, adding some more recent findings in order to give a clear vision of directions for future work in this field of research.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
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ImageNet Large Scale Visual Recognition Challenge
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(2016) · Cited by 31,590
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Metadata
- Authors
- Ribani, R., & Marengoni, M.
- Venue
- 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 47-57
- OpenAlex
- W2993751684
- Cited by
- 303
- References
- 83
- FWCI
- 8.21
- Type
- conference paper
- Language
- en
- Open access
- closed
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