Abstract: A generation function to generate and output generated data from an input, a discrimination function to cause each discriminator to discriminate whether the data to be discriminated is based on the training data or the generated data and to output a discrimination result. Also an update function to update the discriminator that has output the discrimination result such that the data to be discriminated is discriminated with higher accuracy, and to further update the generator to increase a probability of discriminating that the generated data-based data to be discriminated is the training data-based data, and a whole update function to cause the updates to be executed for the generator and all the discriminators.

Abstract: A method of generating a three-dimensional model of an object, is executed by a processor. The method includes executing rendering of the three-dimensional model of the object based on an image captured by the imaging device; and modifying the three-dimensional model.

Abstract: [Problem] To provide a learning device for performing more efficient machine learning. [Solution] A learning device unit according to one embodiment comprises at least one learning device and a connection device for connecting an intermediate learning device having an internal state shared by another learning device unit to the at least one learning device.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: According to one embodiment, a communication device includes: acquisition circuitry including a plurality of data acquirers configured to acquire data for transmission; processing circuitry configured to determine consecutive first sequence numbers for a plurality of pieces of the data acquired by the plurality of data acquirers, and to generate a plurality of packets that include the data acquired by the plurality of data acquirers and the first sequence numbers determined for the plurality of pieces of the data; and transmitting circuitry configured to transmit the plurality of packets wherein the packet includes an identifier that identifies the data acquirer having acquired the data in the packet or an identifier that identifies an application corresponding to the data in the packet.

Abstract: [Problem] To provide a learning device for performing more efficient machine learning. [Solution] A learning device unit according to one embodiment comprises at least one learning device and a connection device for connecting an intermediate learning device having an internal state shared by another learning device unit to the at least one learning device.

Abstract: An information processing system according to an embodiment includes processing circuitry. The processing circuitry determines whether or not processing related to an object disposed in an environment is appropriate based on information related to the object. When determining that the processing is not appropriate, the processing circuitry adds label information designated by a user to data on the object.

Abstract: A line drawing automatic coloring method according to the present disclosure includes: acquiring line drawing data of a target to be colored; receiving at least one local style designation for applying a selected local style to at least one place of the acquired line drawing data; and performing coloring processing reflecting the local style designation on the line drawing data based on a learned model for coloring in which it is learned in advance using the line drawing data and the local style designation as inputs.

Abstract: A rendering device includes at least one memory and at least one processor. The at least one processor acquires information about projection data in a 2D space from information about a region of a 3D model; stores the information about the projection data in association with the information about the region in the at least one memory; and generates the projection data based on the information about the projection data. The associated information includes information associating an identifier given to a part of regions obtained by dividing the 3D model with information about a position where the part of the regions is projected in the 2D space.

Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: The disclosure relates to data processing methods, computer readable hardware storage devices, and systems for correlating data corresponding to levels of biomarkers with various breast diseases.

Abstract: A gaze point estimation processing apparatus in an embodiment includes a storage configured to store a neural network as a gaze point estimation model and one or more processors. The storage stores a gaze point estimation model generated through learning based on an image for learning and information relating to a first gaze point for the image for learning. The one or more processors estimate information relating to a second gaze point with respect to an image for estimation from the image for estimation using the gaze point estimation model.

Abstract: A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

Abstract: An information processing device includes a memory, and processing circuitry coupled to the memory. The processing circuitry is configured to acquire gradation processing target image data, and perform gradation processing on the gradation processing target image data based on a learned model learned in advance.

Abstract: A line drawing automatic coloring method according to the present disclosure includes: acquiring line drawing data of a target to be colored; receiving at least one local style designation for applying a selected local style to at least one place of the acquired line drawing data; and performing coloring processing reflecting the local style designation on the line drawing data based on a learned model for coloring in which it is learned in advance using the line drawing data and the local style designation as inputs.

Abstract: One aspect of the present disclosure relates to a data compression method. The method includes generating, by one or more processors, compressed data from data, wherein the compressed data includes one or more unduplicated values of the data and generating, by the one or more processors, index data from the data, wherein the index data includes indices indicative of storage locations for the unduplicated values.

Abstract: An inferring device includes one or more memories and one or more processors. The one or more processors input a vector relating to an atom into a first network which extracts a feature of the atom in a latent space from the vector relating to the atom, and infer the feature of the atom in the latent space through the first network.

Abstract: A flexible data editing scheme to change and modify an intermediate representation or conditional information for a portion of to-be-edited data is disclosed. One aspect of the present disclosure relates to a data editing apparatus, comprising: one or more memories; and one or more processors configured to receive a change indication to change at least a first data area of first data; generate second data by using one or more generative models and an intermediate representation for the first data area; and replace the first data area of the first data with the second data to generate third data.

Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: Provided are a training device, a disease affection determination device, a machine learning method, and a program that are applicable to various living organisms other than humans without performing time-consuming mapping. The present disclosure provides a machine learning unit that trains a model for a predetermined disease using, as an input, a training feature vector based on an appearance frequency of a plurality of types of substrings in a base sequence obtained from a training sample collected from a learning subject and, as an output, label information indicating whether the learning subject is a subject affected by the predetermined disease or a subject not affected by the predetermined disease.