We have the great honor to welcome Jonghyun Choi to our lab for an invited presentation!

Abstract: Continual learning, especially class-incremental learning uses an episodic memory for past knowledge for better performance. Updating a model with the episodic memory is similar to (1) updating a model with past knowledge in the memory by a few-shot learning scheme, and (2) learning an imbalanced distribution of past data and the present data. We address the unrealistic factors in popular continual learning setups and propose a few ideas to make the continual learning research in realistic scenarios.

Short Bio: Jonghyun received the B.S. and M.S. degrees in electrical engineering and computer science from Seoul National University, Seoul, South Korea in 2003 and 2008, respectively. He received a Ph.D. degree from University of Maryland, College Park in 2015, under the supervision of Prof. Larry S. Davis. He is currently an associate professor at Yonsei University, Seoul, South Korea. During his PhD, he has worked as a research intern in US Army Research Lab (2012), Adobe Research (2013), Disney Research Pittsburgh (2014) and Microsoft Research Redmond (2014). He was a senior researcher at Comcast Applied AI Research, Washington, DC from 2015 to 2016. He was a research scientist at Allen Institute for Artificial Intelligence (AI2), Seattle, WA from 2016 to 2018 and is currently an affiliated research scientist. He was an assistant professor at GIST, South Korea. His research interest includes visual recognition using weakly supervised data for semantic understanding of images and videos and visual understanding for edge devices and household robots.

References

https://ppolon.github.io

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Music Reference: 
Damiano Baldoni - Thinking of You (Intro)
Damiano Baldoni - Poenia (Outro)