Title:
INFORMATION PROCESSING METHOD, INFORMATION PROCESSING DEVICE, AND PROGRAM
Document Type and Number:
WIPO Patent Application WO/2021/215261
Kind Code:
A1
Abstract:
An information processing method according to an embodiment of the present technology is executed by a computer system, and includes an extraction step and a generation step. In the extraction step, two or more sets of teacher data are extracted from a plurality of sets of teacher data for training a machine learning model which predicts a correct answer with respect to an input. In the generation step, teacher data for reliability prediction, in which learning data and the reliability of the correct answer prediction are associated with one another, are generated from the two or more extracted sets of teacher data, using a kernel function.
Inventors:
ISHII MASATO (JP)
Application Number:
PCT/JP2021/014910
Publication Date:
October 28, 2021
Filing Date:
April 08, 2021
Export Citation:
Assignee:
SONY GROUP CORP (JP)
International Classes:
G06N20/00
Other References:
HONGYU GUO; YONGYI MAO; RICHONG ZHANG: "MixUp as Locally Linear Out-Of-Manifold Regularization", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 7 September 2018 (2018-09-07), 201 Olin Library Cornell University Ithaca, NY 14853 , XP081427422
HONGYI ZHANG; MOUSTAPHA CISSE; YANN N. DAUPHIN; DAVID LOPEZ-PAZ: "mixup: Beyond Empirical Risk Minimization", ARXIV.ORG, 25 October 2017 (2017-10-25), 201 Olin Library Cornell University Ithaca, NY 14853 , XP081313428
MAEDA, IWAO: "4I2-J-2-01Importance of uncertainty assessment in deep learning", PROCEEDINGS OF THE 33RD ANNUAL CONFERENCE OF THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE; JUNE 4-7, 2019, no. 33, 2019, pages 1 - 4, XP009531486, DOI: 10.11517/pjsai.JSAI2019.0_4I2J201
HONGYI ZHANG; MOUSTAPHA CISSE; YANN N. DAUPHIN; DAVID LOPEZ-PAZ: "mixup: Beyond Empirical Risk Minimization", ARXIV.ORG, 25 October 2017 (2017-10-25), 201 Olin Library Cornell University Ithaca, NY 14853 , XP081313428
MAEDA, IWAO: "4I2-J-2-01Importance of uncertainty assessment in deep learning", PROCEEDINGS OF THE 33RD ANNUAL CONFERENCE OF THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE; JUNE 4-7, 2019, no. 33, 2019, pages 1 - 4, XP009531486, DOI: 10.11517/pjsai.JSAI2019.0_4I2J201
Attorney, Agent or Firm:
OMORI, Junichi (JP)
Download PDF: