Web: http://arxiv.org/abs/2209.07837

Sept. 19, 2022, 1:14 a.m. | Rundong He, Rongxue Li, Zhongyi Han, Yilong Yin

cs.CV updates on arXiv.org arxiv.org

Out-of-distribution (OOD) detection is the key to deploying models safely in
the open world. For OOD detection, collecting sufficient in-distribution (ID)
labeled data is usually more time-consuming and costly than unlabeled data.
When ID labeled data is limited, the previous OOD detection methods are no
longer superior due to their high dependence on the amount of ID labeled data.
Based on limited ID labeled data and sufficient unlabeled data, we define a new
setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD). To …

arxiv detection distribution weakly-supervised

More from arxiv.org / cs.CV updates on arXiv.org

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Tech Business Data Analyst

@ Fivesky | Alpharetta, GA

Senior Applied Scientist

@ Amazon.com | London, England, GBR

AI Researcher (Junior/Mid-level)

@ Charles River Analytics Inc. | Cambridge, MA

Data Engineer - Machine Learning & AI

@ Calabrio | Minneapolis, Minnesota, United States