Fujitsu Corporation

Kumamoto University

Takashi Katoh

Hiroya Inakoshi

Kento Uemura

Suguru Yasutomi

Shinichiro Tago

Tatsuya Asai

Hiroaki Morikawa

Nobuhiro Yugami

Toshio Endoh

Keisuke Goto

Junichi Shigezumi

Kazuki Iwamoto

Yuhei Umeda

Masataka Matsuura

Akira Ura

Takeshi Osoekawa

Yasushi Sakurai

Yasuko Matsubara

Yasuaki Irifune

Saeru Yamamuro

Kouki Kawabata

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.

Computer-readable recording medium, method for learning, and learning device

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.

Learning method, learning apparatus, and computer-readable recording medium

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.

Learning method, and learning apparatus, and recording medium

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.

Learning method, non-transitory computer readable recording medium, and learning device

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.

Learning apparatus, learning method, and recording medium

An adaptability calculation device inputs input data to a learning model and an encoder of an autoencoder that have performed learning with learning data, inputs an output from the learning model and an output from the encoder of the autoencoder to a decoder of the autoencoder, and calculates adaptability of the output from the learning model to the input data based on an output from the decoder and the input data.

Adaptability calculation method, adaptability calculation device, identification method, identification device, and computer-readable recording medium

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.

Sequential learning maintaining a learned concept

An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.

Allocation method, allocating device, and computer-readable recording medium

A learning device learns at last one parameter of a learning model such that each intermediate feature quantity becomes similar to a reference feature quantity, the each intermediate feature quantity being calculated as a result of inputting a plurality of sets of augmentation training data to a first neural network in the learning model, the plurality of augmentation training data being generated by performing data augmentation based on same first original training data. The learning device learns at last one parameter of a second network, in the learning model, using second original training data, which is different than the first original training data, and using the reference feature quantity.

Method, recording medium, and device for utilizing feature quantities of augmented training data

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.

Non-transitory computer-readable storage medium, learning method, and learning device

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.

Training apparatus, training method, and non-transitory computer-readable recording medium

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.

An estimation method implemented by a computer, the estimation method includes: executing learning processing by training an autoencoder with a data group corresponding to a specific task; calculating a degree of compression of each part regarding data included in the data group by using the trained autoencoder; and estimating a common part with another piece of data included in the data group regarding the data corresponding to the specific task based on the calculated degree of compression of each part.

Estimation apparatus, estimation method, and non-transitory computer-readable storage medium for storing estimation program

An optional array in a memory includes an array having blocks each including an address word and a data word, and a boundary that is a position where a ratio between the numbers of unwritten blocks in M area and written blocks in W area is an integer ratio. The controlling process includes when a second write for writing a special value in a written block in the second area is invoked, executing a shrink process of shifting the boundary to shrink the first area; in a case where the first adjacent block at the boundary is a written block, storing an address of the first adjacent block and of a first link destination block forming a link with the write destination block in address words of the first link destination block and of the first adjacent block respectively to form a link.

Array control program, array control method, and array control apparatus

An object detection device extracts feature for input data utilizing an encoder, the input data including labeled data and unlabeled data and detects object in each of the input data, utilizing an object detector. The object detection device generates region data for each of the input data, each of the region data corresponding to the detected object and generates restoration data from the region data and meta-information related to the detected object for each of the input data utilizing a decoder corresponding to the encoder. The object detection device executes learning of the encoder and the object detector based on a result detected by the object detector and a label associated with the input data, when the input data is labeled data. The object detection device executes learning of the encoder, the object detector, and the decoder, based on the input data and the restoration data.

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Yokohama, Japan

Takashi Katoh