TRAINING DEVICE, ABNORMALITY DETECTION DEVICE, TRAINING METHOD, ABNORMALITY DETECTION METHOD, AND PROGRAM

Title:

TRAINING DEVICE, ABNORMALITY DETECTION DEVICE, TRAINING METHOD, ABNORMALITY DETECTION METHOD, AND PROGRAM

Document Type and Number:

WIPO Patent Application WO/2023/073867

Kind Code:

A1

Abstract:

A training device according to one embodiment of the present invention has: an input unit for inputting a set of normal data of a first system serving as a target domain and a set of normal data of a second system serving as a source domain; and a training unit for training a model comprising a first encoder for receiving input of data of the target domain and a second encoder for receiving input of data of the source domain by using the set of normal data of the first system and the set of normal data of the second system, a discriminator for receiving input of output data of either the first encoder or the second encoder and determining whether the output data represents a feature of either the target domain or the source domain, and Deep SVDD for receiving input of the output data.

Inventors:

MATSUO YOICHI (JP)
TAJIRI KENGO (JP)

Application Number:

PCT/JP2021/039838

Publication Date:

May 04, 2023

Filing Date:

October 28, 2021

Export Citation:

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Assignee:

NIPPON TELEGRAPH & TELEPHONE (JP)

International Classes:

G06N20/00

Foreign References:

JP2020140279A	2020-09-03
US20200285997A1	2020-09-10

Other References:

RUFF LUKAS, ROBERT VANDERMEULEN, NICO GOERNITZ, LUCAS DEECKE, SHOAIB AHMED SIDDIQUI, ALEXANDER BINDER, EMMANUEL MÜLLER, MARIUS KLO: "Deep One-Class Classification", PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING, PMLR, vol. 80, 1 January 2018 (2018-01-01), pages 4393 - 4402, XP093060329, DOI: 10.48550/arxiv.2007.01760