[Prof. Sung-Ju Lee, Prof. Jinwoo Shin, Taesik Gong, Jongheon Jeong, Yewon Kim ,Taewon Kim, from left]
A research team led by Professor Sung-Ju Lee of the School of Electrical and Electronic Engineering and Professor Jinwoo Shin of the Graduate School of AI developed a test-time adaptation artificial intelligence technology that adapts itself to environmental changes.
The algorithm proposed by the research team showed an average improvement of 11% in accuracy compared to the existing best performing algorithm.
Titled “NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation,” this study will be presented in December at ‘NeurIPS (NeurIPS) 2022’, one of the most prestigious international conferences in the field of artificial intelligence.
Dr. Taeshik Gong led the research as the first author, and Jongheon Jeong, Taewon Kim, and Yewon Kim contributed as co-authors.
Professor Sung-Ju Lee and Professor Jinwoo Shin said, “Test time domain adaptation is a technology that allows artificial intelligence to adapt itself to changes in the environment and improve its performance, and its uses are limitless. The NOTE technology to be announced is the first technology to show performance improvement in actual data distribution, and is expected to be applicable to various fields such as autonomous driving, artificial intelligence medical care, and mobile health care.”