Presentation	2020-09-09 [Invited Talk] Understanding Sensitivity through Deep Learning Kensuke Tobitani,
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Abstract(in Japanese)	(See Japanese page)
Abstract(in English)	In recent years, machine learning techniques such as deep learning have been applied to various tasks that could not be performed by computers. The field of sensitivity information processing is no exception, and some researches are attempting to understand human sensitivity, which is considered to be a weak causality, using such technologies. In this talk, I will introduce the technical aspects of the techniques based on the examples we have conducted so far.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	deep learning / sensitivity / estimation of impression
Paper #	MVE2020-12
Date of Issue	2020-09-01 (MVE)

Conference Information
Committee	MVE
Conference Date	2020/9/8(2days)
Place (in Japanese)	(See Japanese page)
Place (in English)	Online
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair	Masayuki Ihara(NTT)
Vice Chair	Kiyoshi Kiyokawa(NAIST)
Secretary	Kiyoshi Kiyokawa(Oosaka Inst. of Tech.)
Assistant	Naoya Isoyama(NAIST) / Takenori Hara(DNP) / Mitsuhiro Goto(NTT)

Paper Information
Registration To	Technical Committee on Media Experience and Virtual Environment
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	[Invited Talk] Understanding Sensitivity through Deep Learning
Sub Title (in English)
Keyword(1)	deep learning
Keyword(2)	sensitivity
Keyword(3)	estimation of impression
1st Author's Name	Kensuke Tobitani
1st Author's Affiliation	University of Nagasaki/Kwansei Gakuin University(Univ. of Nagasaki/Kwansei Gakuin Univ.)
Date	2020-09-09
Paper #	MVE2020-12
Volume (vol)	vol.120
Number (no)	MVE-160
Page	pp.pp.14-14(MVE),
#Pages	1
Date of Issue	2020-09-01 (MVE)