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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ICSS, IPSJ-SPT | 2024-03-22 11:20 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
Evaluation of Feature Inference Risk from Explainable AI metrics LIME and Shapley Values Ryotaro Toma, Hiroaki Kikuchi (Meiji Univ.)  ICSS2023-88 |
Explainability has gained attention to ensure fairness and transparency in machine learning models, providing users with... [more] | ICSS2023-88 pp.137-144 |
| CQ | 2023-07-13 10:50 |
Hokkaido | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Interaction between Human and Autonomous Artifacts from the Perspective of Cognitive Psychology Akihiro Maehigashi (Shizuoka Univ.) CQ2023-21 |
This paper showed the related works about trust in humans-AI interaction and system designs for trust calibration and in... [more] | CQ2023-21 pp.71-73 |
| MI | 2023-03-07 08:56 |
Okinawa | OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
MI2022-103 |
(To be available after the conference date) [more] | MI2022-103 pp.129-130 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:10 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Analysis of TV commercial favorability by scene labeling and XAI Kanta Fukuyori, Kunio Matsui (Knazawa Inst.Tech.), Koh Tatsumoto (TOKYO KIKAKU CO.,Ltd.) PRMU2022-116 IBISML2022-123 |
A large amount of money (6 trillion yen) is spent every year to produce TV commercials that advertise companies and thei... [more] | PRMU2022-116 IBISML2022-123 pp.305-310 |
| KBSE, SWIM | 2022-05-20 15:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
Practical Application of Self-Adaptive Anomaly Detection Method Using XAI Shimon Sumita, Hiroyuki Nakagawa, Tatsuhiro Tsuchiya (Osaka Univ.) KBSE2022-3 SWIM2022-3 |
In this study, we examine the use of XAI to improve the performance of a self-adaptive anomaly detection method. As a sp... [more] | KBSE2022-3 SWIM2022-3 pp.13-18 |
| SIS, ITE-BCT | 2021-10-08 10:00 |
Online | Online | [Tutorial Lecture]
The Past and The Future of Explainable AI Techniques Yoshitaka Kameya (Meijo Univ.) SIS2021-17 |
Machine learning models of high predictive performance, such as deep neural networks and ensemble models, now play a cen... [more] | SIS2021-17 pp.36-41 |
| PRMU | 2020-12-18 17:00 |
Online | Online | An evaluation method of area detection AI based on contribution pattern variation with noise addition Yasuhide Mori, Naofumi Hama, Masashi Egi (Hitachi) PRMU2020-67 |
The processing of image recognition AI using machine learning is generally black-boxed, and grasping the operating chara... [more] | PRMU2020-67 pp.166-171 |
| IBISML | 2020-10-22 14:50 |
Online | Online | Suppressing explanations with irrelevant concepts in deep learning Munemasa Tomohiro (Tsukuba Univ), Fukuchi Kazuto, Akimoto Yohei, Sakuma Jun (Tsukuba Univ/Riken AIP) IBISML2020-32 |
TCAV [1], which is an explanation method using a concept that humans easily understand for deep learning models, concept... [more] | IBISML2020-32 pp.61-68 |
| MI, IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2020-05-29 14:30 |
Online | Online | A method for analyze causes of deterioration of predict quality when Deep Learning is applied to instance segmentation Tomonori Kubota, Takanori Nakao, Masafumi Katoh, Eiji Yoshida, Hidenobu Miyoshi (Fujitsu Lab.) SIP2020-14 BioX2020-14 IE2020-14 MI2020-14 |
In this paper, we propose a method to analyze the cause of deterioration of prediction accuracy in instance segmentation... [more] | SIP2020-14 BioX2020-14 IE2020-14 MI2020-14 pp.67-72 |
| IE, IMQ, MVE, CQ (Joint) [detail] |
2020-03-06 14:50 |
Fukuoka | Kyushu Institute of Technology (Cancelled but technical report was issued) |
A high-compression video coding method for video analysis using Deep Learning Tomonori Kubota, Takanori Nakao, Eiji Yoshida (Fujitsu Lab.) IMQ2019-39 IE2019-121 MVE2019-60 |
In this paper, we propose a high-compression video coding method for video analysis using Deep Learning. The method anal... [more] | IMQ2019-39 IE2019-121 MVE2019-60 pp.121-126 |
| AI | 2019-11-28 13:05 |
Fukuoka | A proposal of a method for analyzing causes of incorrect detection when detecting objects using Deep Learning Tomonori Kubota, Takanori Nakao, Eiji Yoshida (Fujitsu Lab.) AI2019-30 |
In this paper, we propose a method for analyzing the causes of incorrect detection / poor accuracy when detecting object... [more] | AI2019-30 pp.1-6 |
|
| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 13:40 |
Okayama | A method for visualizing the cause of misrecognition in object recognition using CNN Tomonori Kubota (Fujitsu Lab.), Yasuyuki Murata (FST), Yoshifumi Uehara, Akira Nakagawa (Fujitsu Lab.) PRMU2019-25 MI2019-44 |
In this paper, we propose a method for visualizing the cause of misrecognition in object recognition using CNN. By this ... [more] | PRMU2019-25 MI2019-44 pp.99-104 |
|
| PRMU, BioX | 2019-03-17 14:45 |
Tokyo | A Study of Business Interpretation Technique for AI Predictions Naoaki Yokoi, Masashi Egi (Hitachi, Ltd.) BioX2018-39 PRMU2018-143 |
(To be available after the conference date) [more] | BioX2018-39 PRMU2018-143 pp.61-66 |
|
| EA, US (Joint) |
2006-01-27 10:30 |
Kyoto | Minimum Error Relaxation Algorithm of Inverse Filter in Multi-Channel Sound Reproduction System Yusuke Kaibara, Shigeki Miyabe, Hiroshi Saruwatari, Kiyohiro Shikano (NAIST), Yosuke Tatekura (Shizuoka Univ.) |
In this paper we propose a new adaptive alorithm of relaxing inverse filter for multi-channel sound field reproduction ... [more] |
EA2005-97 pp.7-12 |
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