公开(公告)号：US20240095906A1

公开(公告)日：2024-03-21

申请号：US18511337

申请日：2023-11-16

Applicant: Panasonic Intellectual Property Corporation of America

Inventor： Denis Gudovskiy ,  Shun Ishizaka ,  Kazuki Kozuka

IPC: G06T7/00 ,  G06N3/0464 ,  G06V10/44

CPC classification number: G06T7/001 ,  G06N3/0464 ,  G06V10/454 ,  G06T2207/20084

Abstract: An anomaly detection method by which a computer performs anomaly detection includes: obtaining first feature data outputted through N (N is an integer not less than 1) convolutional layers of a convolutional neural network configured as an encoder when an image is inputted to the convolutional neural network; obtaining second feature data outputted through M (M is an integer not less than 1, and M≠N) convolutional layers of the convolutional neural network and different in size from the first feature data; and performing anomaly detection on the image by using features indicated by the first feature data and the second feature data that are different in size.

公开(公告)号：US11449706B2

公开(公告)日：2022-09-20

申请号：US16849334

申请日：2020-04-15

Applicant: Panasonic Intellectual Property Corporation of America

Inventor： Denis Gudovskiy ,  Takuya Yamaguchi ,  Yasunori Ishii ,  Sotaro Tsukizawa

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00 ,  G06V20/10

Abstract: An information processing method performed by a computer includes: obtaining a plurality of recognition result candidates in sensing data and a likelihood of each of the plurality of recognition result candidates, the plurality of recognition result candidates and the likelihood being obtained by inputting the sensing data to a model that is trained by machine learning and performs recognition processing; obtaining an indication designating a part to be analyzed in the sensing data; selecting at least one recognition result candidate from the plurality of recognition result candidates, based on (i) a relationship between each of the plurality of recognition result candidates and the part and (ii) the likelihood of each of the plurality of recognition result, candidates; and outputting the at least one recognition result candidate that is selected.

公开(公告)号：US11995150B2

公开(公告)日：2024-05-28

申请号：US17234127

申请日：2021-04-19

Applicant: Panasonic Intellectual Property Corporation of America

Inventor： Denis Gudovskiy ,  Alec Hodgkinson ,  Takuya Yamaguchi ,  Sotaro Tsukizawa

IPC: G06F18/214 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06F18/214 ,  G06N5/04 ,  G06N20/00

Abstract: An information processing method implemented by a computer includes: obtaining a piece of first data, and a piece of second data not included in a training dataset for training an inferencer; calculating, using a piece of first relevant data obtained by inputting the first data to the inferencer trained by machine learning using the training dataset, a first contribution representing contributions of portions constituting the first data to a piece of first output data output by inputting the first data to the inferencer; calculating, using a piece of second relevant data obtained by inputting the second data to the inferencer, a second contribution representing contributions of portions constituting the second data to a piece of second output data output by inputting the second data to the inferencer; and determining whether to add the second data to the training dataset, according to the similarity between the first and second contributions.

公开(公告)号：US20240086774A1

公开(公告)日：2024-03-14

申请号：US18504300

申请日：2023-11-08

Applicant: Panasonic Intellectual Property Corporation of America

Inventor： Konstantinos Karras Kallidromitis ,  Denis Gudovskiy ,  Iku Ohama ,  Kazuki Kozuka

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A training method performed through batch learning by a computer includes: obtaining training data including first time-series data and second time-series data different from the first time-series data; performing first training processing of training a neural process (NP) model, which outputs, using a stochastic process, a prediction result that takes uncertainty into account, to predict first and second time-series data distributions, based on the first time-series data and second time-series data; and performing, using a contrastive learning algorithm, second training processing of (i) training the NP model to bring close to each other first sampling data items generated by sampling from the first time-series data distribution, (ii) training the NP model to bring close to each other second sampling data items generated by sampling from the second time-series data distribution, and (iii) training the NP model to push away the first and second sampling data items far from each other.