The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Simultaneous Deep Transfer Across Domains and Tasks
Open accessAbstract
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings.
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Cited by
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154
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Sun, B., & Saenko, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_35 · Cited by 3,757
Self-citationA Survey on Deep Transfer Learning
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Deep visual domain adaptation: A survey
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Metadata
- Authors
- Tzeng, E., Hoffman, J., Darrell, T., & Saenko, K.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 4068-4076
- OpenAlex
- W2214409633
- Cited by
- 1,149
- References
- 10
- FWCI
- 41.66
- Type
- conference paper
- Language
- en
- Open access
- green
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