Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data-Reference-Cited by-同舟云学术

Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Published:2020-08-20 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Wilson Garrett¹,Doppa Janardhan Rao¹,Cook Diane J.¹

Affiliation:

1. Washington State University, Pullman, WA, USA

Funder

National Science Foundation

National Institutes of Health

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403228

Reference43 articles.

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2. S. Bai J. Z. Kolter etal 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271(2018). S. Bai J. Z. Kolter et al. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271(2018).

3. A theory of learning from different domains

4. K. Bousmalis etal 2017. Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks. In CVPR. K. Bousmalis et al. 2017. Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks. In CVPR.

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