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1.发明公开公开(公告)号：US20230259827A1
公开(公告)日：2023-08-17
申请号：US18301582
申请日：2023-04-17
Applicant: FUJITSU LIMITED
Inventor： Takashi KATOH , Kento UEMURA , Suguru YASUTOMI , Tomohiro HAYASE
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A non-transitory computer-readable recording medium stores a generation program for causing a computer to execute a process including: with data included in each of a plurality of data sets, training a feature space in which a distance between pieces of the data included in a same domain is shorter and the distance of the data between different domains is longer; and generating labeled data sets by integrating labeled data included within a predetermined range in the trained feature space, among a plurality of pieces of the labeled data.
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2.发明申请公开(公告)号：US20210232854A1
公开(公告)日：2021-07-29
申请号：US17228517
申请日：2021-04-12
Applicant: FUJITSU LIMITED
Inventor： Kento UEMURA , Suguru YASUTOMI , TAKASHI KATOH
Abstract: A non-transitory computer-readable recording medium recording a learning program for causing a computer to execute processing includes: generating restored data using a plurality of restorers respectively corresponding to a plurality of features from the plurality of features generated by a machine learning model corresponding to each piece of input data, for each piece of the input data input to the machine learning model; and making the plurality of restorers perform learning so that each of the plurality of pieces of restored data respectively generated by the plurality of restorers approaches the input data.
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3.发明申请公开(公告)号：US20200234122A1
公开(公告)日：2020-07-23
申请号：US16742033
申请日：2020-01-14
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kento UEMURA , Suguru YASUTOMI
Abstract: A learning device generates a first feature value and a second feature value by inputting original training data to a first neural network included in a learning model. The learning device learns at least one parameter of the learning model and a parameter of a decoder, reconstructing data inputted to the first neural network, such that reconstruction data outputted from the decoder by inputting the first feature value and the second feature value to the decoder becomes close to the original training data, and that outputted data that is outputted from a second neural network, included in the learning model by inputting the second feature value to the second neural network becomes close to correct data of the original training data.
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公开(公告)号：US20180300632A1
公开(公告)日：2018-10-18
申请号：US15947423
申请日：2018-04-06
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kento UEMURA , Suguru YASUTOMI , Toshio Endoh
Abstract: A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process including obtaining a feature quantity of input data by using a feature generator, generating a first output based on the feature quantity by using a supervised learner for labeled data, generating a second output based on the feature quantity by using an unsupervised learning processing for unlabeled data, and changing a contribution ratio between a first error and a second error in a learning by the feature generator, the first error being generated from the labeled data and the first output, the second error being generated from the unlabeled data and the second output.
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公开(公告)号：US20170337203A1
公开(公告)日：2017-11-23
申请号：US15496591
申请日：2017-04-25
Applicant: FUJITSU LIMITED
Inventor： Kento UEMURA , Yuiko Ohta , Keisuke Goto , Hiroya Inakoshi
IPC: G06F17/30
CPC classification number: G06F16/24578 , G06F16/215 , G06F16/221 , G06F16/248 , G06F16/90344
Abstract: An evaluation method which is executed by a processor, the method includes: comparing values of cells between a plurality of pieces of data each including a plurality of cells divided by a plurality of columns and a plurality of records; storing, in a storage unit, information that indicates a plurality of cell sets that have been detected as sets of cells including similar character strings by the comparing; and setting, with reference to the storage unit, a score of each of a plurality of column sets formed by making each of columns of one of the plurality of pieces of data and each of columns of another one of the plurality of pieces of data as a set, based on a score for a record set of records in which a cell set, among the plurality of cell sets, which is included in the column set is included.
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Patent Agency Ranking
公开(公告)号：US20230306037A1
公开(公告)日：2023-09-28
申请号：US18326615
申请日：2023-05-31
Applicant: FUJITSU LIMITED
Inventor： Kento UEMURA , Yusuke KOYANAGI , Kotaro OHORI
IPC: G06F16/248 , G06F16/28
CPC classification number: G06F16/248 , G06F16/284
Abstract: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing including: extracting, for a plurality of pieces of combination data each of which is a combination of a plurality of feature amounts that includes an invariable feature amount and a variable feature amount that represent features of a target, combination data to be processed based on the plurality of pieces of combination data according to relation between the respective pieces of combination data; executing causal search processing for the variable feature amount according to the invariable feature amount by using the combination data to be processed; and selecting, based on a result of the causal search processing, a specific variable feature amount that corresponds to the specified invariable feature amount, to present the selected specific variable feature.
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公开(公告)号：US20220261690A1
公开(公告)日：2022-08-18
申请号：US17542420
申请日：2021-12-05
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kento UEMURA , Suguru YASUTOMI , Tomohiro Hayase
Abstract: A computer-implemented method of a determination processing, the method including: calculating, in response that deterioration of a classification model has occurred, a similarity between a first determination result and each of a plurality of second determination results, the first determination result being a determination result output from the classification model by inputting first input data after the deterioration has occurred to the classification model, and the plurality of second determination results being determination results output from the classification model by inputting, to the classification model, a plurality of pieces of post-conversion data converted by inputting second input data before the deterioration occurs to a plurality of data converters; selecting a data converter from the plurality of data converters on the basis of the similarity; and preprocessing in data input of the classification model by using the selected data converter.
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公开(公告)号：US20220076162A1
公开(公告)日：2022-03-10
申请号：US17381889
申请日：2021-07-21
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kento UEMURA , Suguru YASUTOMI , Tomohiro Hayase
Abstract: A non-transitory computer-readable storage medium storing a data presentation program that causes at least one computer to execute a process, the process includes acquiring certain data from an estimation target data set that uses an estimation model, based on an estimation result for the estimation target data set; and presenting data obtained by changing the certain data in a direction orthogonal to a direction in which loss of the estimation model fluctuates, in a feature space that relates to feature amounts obtained from the estimation target data set.
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公开(公告)号：US20200250544A1
公开(公告)日：2020-08-06
申请号：US16780975
申请日：2020-02-04
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kento UEMURA , Suguru YASUTOMI , Takuya Takagi , KEN KOBAYASHI , Akira URA , Kenichi KOBAYASHI
Abstract: A learning method executed by a computer, the learning method includes inputting a first data being a data set of transfer source and a second data being one of data sets of transfer destination to an encoder to generate first distributions of feature values of the first data and second distributions of feature values of the second data; selecting one or more feature values from among the feature values so that, for each of the one or more feature values, a first distribution of the feature value of the first data is similar to a second distribution of the feature value of the second data; inputting the one or more feature values to a classifier to calculate prediction labels of the first data; and learning parameters of the encoder and the classifier such that the prediction labels approach correct answer labels of the first data.
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10.发明申请公开(公告)号：US20200242399A1
公开(公告)日：2020-07-30
申请号：US16774721
申请日：2020-01-28
Applicant: FUJITSU LIMITED
Inventor： TAKASHI KATOH , Kazuki IWAMOTO , Kento UEMURA , Suguru YASUTOMI
Abstract: An anomaly detection apparatus generates pieces of image data using a generator and train the generator and a discriminator that discriminates whether an image data, generated by the generator, is real or fake. The anomaly detection apparatus trains the generator such that the generator, in generating the pieces of image data to maximize the discrimination error of the discriminator, generate at least a piece of specified image data to reduce the discrimination error at a fixed rate with respect to the pieces of image data and trains, based on the pieces of image data and the at least a piece of specified image data, the discriminator to minimize the discrimination error.
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