The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Beyond Sharing Weights for Deep Domain Adaptation
Open accessAbstract
The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractical. Domain Adaptation has therefore emerged as a solution to this problem; It leverages annotated data from a source domain, in which it is abundant, to train a classifier to operate in a target domain, in which it is either sparse or even lacking altogether. In this context, the recent trend consists of learning deep architectures whose weights are shared for both domains, which essentially amounts to learning domain invariant features. Here, we show that it is more effective to explicitly model the shift from one domain to the other. To this end, we introduce a two-stream architecture, where one operates in the source domain and the other in the target domain. In contrast to other approaches, the weights in corresponding layers are related but not shared. We demonstrate that this both yields higher accuracy than state-of-the-art methods on several object recognition and detection tasks and consistently outperforms networks with shared weights in both supervised and unsupervised settings.
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Cites
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SURF: Speeded Up Robust Features
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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
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
A survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987
Unified Deep Supervised Domain Adaptation and Generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., & Doretto, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.609 · Cited by 803
Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544
Metadata
- Authors
- Rozantsev, A., Salzmann, M., & Fua, P.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-04-01
- Volume
- 41
- Issue
- 4
- Pages
- 801-814
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2312004824
- Cited by
- 462
- References
- 73
- FWCI
- 34.23
- Type
- journal article
- Language
- en
- Open access
- green
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