- Abstract:
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A practical limitation of deep neural networks is their high degree of specialization to a single task and visual domain. Recently, inspired by the successes of transfer learning, several authors have proposed to learn instead universal feature extractors that, used as the first stage of any deep network, work well for several tasks and domains simultaneously. Nevertheless, such universal features are still somewhat inferior to specialized networks.
To overcome this limitation...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
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(pdf, 584.3KB)
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- Publisher copy:
- 10.1109/CVPR.2018.00847
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- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Publication date:
- 2018-12-17
- Acceptance date:
- 2018-02-28
- DOI:
- Pubs id:
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pubs:859554
- URN:
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uri:a8e2ae12-2c29-4620-9127-31dc05323add
- UUID:
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uuid:a8e2ae12-2c29-4620-9127-31dc05323add
- Local pid:
- pubs:859554
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2018
- Notes:
- © 2018 IEEE. This is the accepted manuscript version of the article. The final version is available online from Institute of Electrical and Electronics Engineers at: https://doi.org/10.1109/CVPR.2018.00847
Conference item
Efficient parametrization of multi-domain deep neural networks
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