公开(公告)号：US20200242412A1

公开(公告)日：2020-07-30

申请号：US16774100

申请日：2020-01-28

Applicant: FUJITSU LIMITED

Inventor： TAKASHI KATOH ,  Kazuki IWAMOTO ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06K9/62 ,  G06T7/00 ,  G06N20/00 ,  G16H30/40

Abstract: An anomaly detection apparatus performs training for the generator and the discriminator such that the generator maximizes a discrimination error of the discriminator and the discriminator minimizes the discrimination error The anomaly detection apparatus stores, while the training is being performed, a state of the generator that is half-trained and satisfies a pre-set condition, and retrains the discriminator by using an image generated by the half-trained generator that has the stored state.

公开(公告)号：US20200234140A1

公开(公告)日：2020-07-23

申请号：US16741860

申请日：2020-01-14

Applicant: FUJITSU LIMITED

Inventor： TAKASHI KATOH ,  Kento UEMURA ,  Suguru YASUTOMI ,  Takeshi OSOEKAWA

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20

Abstract: A learning method executed by a computer, the learning method includes: learning parameters of a machine learning model having intermediate feature values by inputting a plurality of augmented training data, which is generated by augmenting original training data, to the machine learning model so that specific intermediate feature values, which are calculated from specific augmented training data augmented from a same original training data, become similar to each other.

公开(公告)号：US20200234139A1

公开(公告)日：2020-07-23

申请号：US16741839

申请日：2020-01-14

Applicant: FUJITSU LIMITED

Inventor： TAKASHI KATOH ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06N3/08 ,  G06N20/20 ,  G06N3/04

Abstract: A learning method executed by a computer, the learning method including augmenting original training data based on non-stored target information included in the original training data to generate a plurality of augmented training data, generating a plurality of intermediate feature values by inputting the plurality of augmented training data to a learning model, and learning a parameter of the learning model such that, with regard to the plurality of intermediate feature values, each of the plurality of intermediate feature values generated from a plurality of augmented training data, augmented from reference training data, becomes similar to a reference feature value.

公开(公告)号：US20200226494A1

公开(公告)日：2020-07-16

申请号：US16717563

申请日：2019-12-17

Applicant: FUJITSU LIMITED

Inventor： Suguru YASUTOMI ,  Kento UEMURA ,  TAKASHI KATOH

IPC: G06N20/00 ,  G06T5/50 ,  G06F17/18 ,  A61B8/08

Abstract: A non-transitory computer-readable recording medium stores therein a learning program that causes a computer to execute a process including: generating a shadow image including a shadow according to a state of ultrasound reflection in an ultrasound image; generating a combined image by combining the ultrasound image and the shadow image; inputting, into a first decoder and a second decoder, an output acquired from an encoder in response to inputting the combined image into the encoder; and executing training of the encoder, the first decoder, and the second decoder, based on: reconfigured error between an output image of a coupling function and the combined image, the coupling function being configured to combine a first image output from the first decoder with a second image output from the second decoder, and an error function between an area in the first image and the shadow in the shadow image.

公开(公告)号：US20200042876A1

公开(公告)日：2020-02-06

申请号：US16653236

申请日：2019-10-15

Applicant: FUJITSU LIMITED

Inventor： TAKASHI KATOH ,  Kento UEMURA ,  Suguru YASUTOMI ,  Toshio Endoh ,  Koji MARUHASHI

IPC: G06N3/08 ,  G06K9/62 ,  G05B23/02

Abstract: A non-transitory computer-readable recording medium records an estimation program causing a computer to execute processing which includes: calculating a reconfiguration error from an input result value and a reconfiguration value that is estimated by a first estimator, which estimates a parameter value from a result value learned on a basis of past data, and a second estimator, which estimates a result value from a parameter value, by using a specific result value or a neighborhood result value in a neighborhood of the specific result value; searching for a first result value that minimizes a sum of a substitute error that is calculated from the input result value and the specific result value and the reconfiguration error; and outputting a parameter value that is estimated from the first result value by using the first estimator.

公开(公告)号：US20190286937A1

公开(公告)日：2019-09-19

申请号：US16287494

申请日：2019-02-27

Applicant: FUJITSU LIMITED

Inventor： Takashi KATOH ,  Kento UEMURA ,  Suguru YASUTOMI

IPC: G06K9/62 ,  G06N20/00

Abstract: A learning device executes learning of a discriminator that discriminates object data to a known class included in training data or an unknown class not included in the training data, using the training data. The learning device then generates a feature value of the unknown class, from a feature value of a plurality of layers of the discriminator, by at least a part of the training data in the layers. The learning device then executes the learning of the discriminator so that a feature value of the known class and the generated feature value of the unknown class are separated.