all AI news
Unsupervised Learning of the Total Variation Flow. (arXiv:2206.04406v1 [cs.CV])
June 10, 2022, 1:12 a.m. | Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb
cs.CV updates on arXiv.org arxiv.org
The total variation (TV) flow generates a scale-space representation of an
image based on the TV functional. This gradient flow observes desirable
features for images such as sharp edges and enables spectral, scale, and
texture analysis. The standard numerical approach for TV flow requires solving
multiple non-smooth optimisation problems. Even with state-of-the-art convex
optimisation techniques, this is often prohibitively expensive and strongly
motivates the use of alternative, faster approaches. Inspired by and extending
the framework of physics-informed neural networks (PINNs), …
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Program Control Data Analyst
@ Ford Motor Company | Mexico
Vice President, Business Intelligence / Data & Analytics
@ AlphaSense | Remote - United States