Importance Weighted Adversarial Nets for Partial Domain Adaptation
| Autor: | Wanqing Li, Philip Ogunbona, Jing Zhang, Zewei Ding |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Theoretical computer science Artificial neural network Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) 020208 electrical & electronic engineering Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Domain (software engineering) Outlier 0202 electrical engineering electronic engineering information engineering Task analysis Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence Adaptation (computer science) Divergence (statistics) business Knowledge transfer |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2018.00851 |
| Popis: | This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. This paper extends the adversarial nets-based domain adaptation and proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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