Web: http://arxiv.org/abs/2209.10677

Sept. 23, 2022, 1:14 a.m. | Eric Yeats, Frank Liu, David Womble, Hai Li

cs.CV updates on arXiv.org arxiv.org

We present a self-supervised method to disentangle factors of variation in
high-dimensional data that does not rely on prior knowledge of the underlying
variation profile (e.g., no assumptions on the number or distribution of the
individual latent variables to be extracted). In this method which we call
NashAE, high-dimensional feature disentanglement is accomplished in the
low-dimensional latent space of a standard autoencoder (AE) by promoting the
discrepancy between each encoding element and information of the element
recovered from all other …

arxiv covariance

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