Distributional Shift Adaptation using Domain-Specific Features
Author:
Affiliation:
1. Arizona State University,Tempe,AZ,USA
2. University of Illinois Chicago,Chicago,IL,USA
3. Bytedance AI Lab,London,UK
Funder
Office of Naval Research
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020444.pdf?arnumber=10020444
Reference30 articles.
1. Extending the WILDS benchmark for unsupervised adaptation;sagawa;CoRR,2021
2. WILDS: A benchmark of in-the-wild distribution shifts;koh;CoRR,2020
3. An online learning approach to interpolation and extrapolation in domain generalization;rosenfeld;International Conference on Artificial Intelligence and Statistics,2022
4. The Parable of Google Flu: Traps in Big Data Analysis
5. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks;lee;Workshop on Challenges in Representation Learning,2013
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