Web: http://arxiv.org/abs/2209.07910

Sept. 19, 2022, 1:11 a.m. | Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo

cs.LG updates on arXiv.org arxiv.org

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source …

arxiv consistent image medical memory segmentation unsupervised

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

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

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

Software Engineer, Machine Learning

@ Next Insurance | Atlanta

Big Data Engineer- E4076

@ Nisum | United States

[Job-8613] Data Engineer SR.

@ CI&T | Brazil