公开(公告)号：US20230306306A1

公开(公告)日：2023-09-28

申请号：US18159106

申请日：2023-01-25

Applicant: Fujitsu Limited

Inventor： TAKASHI KATOH ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A non-transitory computer-readable storage medium storing a machine learning program that causes at least one computer to execute a process, the process includes estimating a first label distribution of unlabeled training data based on a classification model and an initial value of a label distribution of a transfer target domain, the classification model being trained by using labeled training data which corresponds to a transfer source domain and unlabeled training data which corresponds to the transfer target domain; acquiring a second label distribution based on the labeled training data; acquiring a weight of each label included in the labeled training data and the unlabeled training data based on a difference between the first label distribution and the second label distribution; and re-training the classification model by the labeled training data and the unlabeled training data reflected the weight of each label.

公开(公告)号：US20230289406A1

公开(公告)日：2023-09-14

申请号：US18177189

申请日：2023-03-02

Applicant: Fujitsu Limited

Inventor： Takashi KATOH ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06F18/2415 ,  G06F18/2431

CPC classification number: G06F18/2415 ,  G06F18/2431

Abstract: A non-transitory computer-readable recording medium stores a determination program for causing a computer to execute processing including: re-training a classification model that has been trained by using a first data set and that classifies input data into any one of a plurality of classes by using a loss calculatable based on a second data set that is different from the first data set; and determining, in a case where a change in a classification standard of the classification model based on the loss is a predetermined standard or more before and after re-training, that unknown data that is not classified into any one of the plurality of classes is included in the second data set.

公开(公告)号：US20230186118A1

公开(公告)日：2023-06-15

申请号：US18157639

申请日：2023-01-20

Applicant: FUJITSU LIMITED

Inventor： Tomohiro HAYASE ,  TAKASHI KATOH ,  Suguru YASUTOMI ,  Kento UEMURA

IPC: G06N5/022

CPC classification number: G06N5/022

Abstract: A program for causing a computer to execute processing including: acquiring a plurality of datasets, each of which includes data values associated with a label, the data values having properties different for each dataset; calculating an index indicating a degree of a difference between first and second datasets by using a data value in the second dataset; calculating accuracy of a prediction result for the second dataset, predicted by a prediction model trained using the first dataset; specifying a relationship between the index and the accuracy of the prediction result from the prediction model, based on the index and the accuracy calculated for each of a plurality of combinations of the first and second datasets; and estimating accuracy of the prediction result from the prediction model for a third dataset including data values without labels based on the specified relationship and the index between the first and third datasets.

公开(公告)号：US20220245405A1

公开(公告)日：2022-08-04

申请号：US17727915

申请日：2022-04-25

Applicant: FUJITSU LIMITED

Inventor： TAKASHI KATOH ,  Kento UEMURA ,  Suguru YASUTOMI ,  Tomohiro Hayase ,  YUHEI UMEDA

IPC: G06K9/62 ,  G06N20/00

Abstract: A deterioration suppression device generates a plurality of trained machine learning models having different characteristics on the basis of each training data included in a first training data set and assigned with a label indicating correct answer information. In a case where estimation accuracy of label estimation with respect to input data to be estimated by any trained machine learning model among the plurality of trained machine learning models becomes lower than a predetermined standard, the deterioration suppression device generates a second training data set including a plurality of pieces of training data using an estimation result by a trained machine learning model with the estimation accuracy equal to or higher than the predetermined standard. The deterioration suppression device executes re-learning of the trained machine learning model with the estimation accuracy lower than the predetermined standard using the second training data set.

公开(公告)号：US20200226796A1

公开(公告)日：2020-07-16

申请号：US16720667

申请日：2019-12-19

Applicant: FUJITSU LIMITED

Inventor： Suguru YASUTOMI

IPC: G06T9/00 ,  G06T11/00 ,  G06T7/00

Abstract: A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: inputting an output from an encoder to which an input image is input to a first decoder and a second decoder; and executing learning of the encoder, the first decoder and the second decoder, based on a reconstruction error between the input image and an output image obtained by using a combining function for synthesizing a first image that is an output from the first decoder and a second image that is an output from the second decoder, based on a first likelihood function for the first image relating to shades in ultrasound images, and based on a second likelihood function for the second image relating to subjects in ultrasound images.

公开(公告)号：US20200160119A1

公开(公告)日：2020-05-21

申请号：US16680562

申请日：2019-11-12

Applicant: FUJITSU LIMITED

Inventor： Hiroya INAKOSHI ,  TAKASHI KATOH ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: An apparatus receives, at a discriminator within a generative adversarial network, first generation data from a first generator within the generative adversarial network, where the first generator has performed learning using a first data group. The apparatus receives, at the discriminator, a second data group, and performs learning of a second generator based on the first generation data and the second data group where the first generation data is handled as false data by the discriminator.

公开(公告)号：US20230289624A1

公开(公告)日：2023-09-14

申请号：US18088825

申请日：2022-12-27

Applicant: Fujitsu Limited

Inventor： Takashi KATOH ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06N5/022

CPC classification number: G06N5/022

Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes acquiring an update amount of a classification criterion of a classification model in retraining, the classification model being trained by using a first dataset, the classification model classifying input data into one of a plurality of classes, the retraining being performed by using a second dataset; and detecting data with a largest change amount among the second dataset when changing each piece of data included in the second dataset so as to decrease the update amount.

公开(公告)号：US20230281845A1

公开(公告)日：2023-09-07

申请号：US18060338

申请日：2022-11-30

Applicant: Fujitsu Limited

Inventor： Suguru YASUTOMI ,  Akira SAKAI ,  Takashi KATOH ,  Kento UEMURA

IPC: G06T7/507 ,  G06T5/50 ,  G06T7/60 ,  G06V10/75

CPC classification number: G06T7/507 ,  G06T5/50 ,  G06T7/60 ,  G06V10/758 ,  G06T2207/10132 ,  G06T2207/20221

Abstract: An information processing device acquires output image data that is acquired by inputting image data indicating a pseudo-shadow area to an auto-encoder that is generated by machine learning using label image data contained in training data, the label image data indicating a shadow area in ultrasound image data of a captured target. The information processing device generates augmented data corresponding to the training data by combining the acquired output image data with the ultrasound image data.

公开(公告)号：US20220147764A1

公开(公告)日：2022-05-12

申请号：US17473509

申请日：2021-09-13

Applicant: FUJITSU LIMITED

Inventor： Takashi KATOH ,  Kento UEMURA ,  Suguru YASUTOMI ,  Tomohiro HAYASE

IPC: G06K9/62 ,  G06F17/17 ,  G06N20/00

Abstract: A non-transitory computer-readable storage medium storing a data generation program that causes at least one computer to execute a process, the process includes, acquiring a data generation model that is trained by using a first dataset corresponding to a first domain and a second dataset corresponding to a second domain, and that includes an identification loss by an identification model in a parameter; inputting first data corresponding to the first domain to the identification model to acquire a first identification loss, and inputting second data corresponding to the second domain to the identification model to acquire a second identification loss; generating data in which the second identification loss approximates the first identification loss, by using the data generation model; and outputting the data that is generated.

公开(公告)号：US20220101124A1

公开(公告)日：2022-03-31

申请号：US17368890

申请日：2021-07-07

Applicant: FUJITSU LIMITED

Inventor： Suguru YASUTOMI ,  Tomohiro HAYASE ,  Takashi KATOH

IPC: G06N3/08 ,  G06N3/04

Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes acquiring a first machine learning model trained by using a training data set including first data and a second machine learning model not trained with the specific data; and retraining the first machine learning model so that an output of the first machine learning model and an output of the second machine learning model when second data corresponding to the first data is input get close to each other.