all AI news
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (arXiv:2111.00131v2 [cs.CV] UPDATED)
Jan. 27, 2022, 2:11 a.m. | Akira Sakai, Taro Sunagawa, Spandan Madan, Kanata Suzuki, Takashi Katoh, Hiromichi Kobashi, Hanspeter Pfister, Pawan Sinha, Xavier Boix, Tomotake Sasa
cs.LG updates on arXiv.org arxiv.org
The training data distribution is often biased towards objects in certain
orientations and illumination conditions. While humans have a remarkable
capability of recognizing objects in out-of-distribution (OoD) orientations and
illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even
when large amounts of training examples are available. In this paper, we
investigate three different approaches to improve DNNs in recognizing objects
in OoD orientations and illuminations. Namely, these are (i) training much
longer after convergence of the in-distribution (InD) …
More from arxiv.org / cs.LG updates on arXiv.org
A Single-Loop Algorithm for Decentralized Bilevel Optimization
1 day, 3 hours ago |
arxiv.org
CLEANing Cygnus A deep and fast with R2D2
1 day, 3 hours ago |
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
Stagista Technical Data Engineer
@ Hager Group | BRESCIA, IT
Data Analytics - SAS, SQL - Associate
@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India