Kioxia Corporation

Kengo Nakata

Daisuke Miyashita

Asuka Maki

Jun Deguchi

Takeshi Fujiwara

Youyang Ng

Bo Wang

Yuchieh Lin

An image processing apparatus has a first image acquisitor that acquires a source image, a second image acquisitor that acquires a first target image, a label acquisitor that acquires a label, a feature extractor including a first neural network that extracts a feature of the source image and a feature of the first target image, a class classifier including a second neural network that performs a class classification of the source image and the first target image, a domain classifier including a third neural network that performs a domain classification of the source image and the first target image, a processor that assigns a pseudo label to the first target image, a self-learner that performs a self-learning of the first neural network, the second neural network, and the third neural network, and a learner that learns the first, second and third neural networks, by performing a back propagation process.

Image processing apparatus and image processing method

According to one embodiment, an information processing method includes: calculating a first feature amount of query data in a first field; calculating first similarity degrees between the first feature amount and second feature amounts in the first field; obtaining, based on the first similarity degrees, third feature amounts in a second field that are associated with feature amounts selected from the second feature amounts, the second field being different from the first field; calculating fourth feature amounts in the second field, for choices concerning the query data; calculating second similarity degrees between the third feature amounts and the fourth feature amounts; and selecting, based on the second similarity degrees, an answer to the query data among answer candidates corresponding to the third feature amounts.

Information processing method, information processing device, and computer system

An arithmetic operation device is provided that removes a part of parameters of a predetermined number of parameters from a first machine learning model which includes the predetermined number of parameters and is trained so as to output second data corresponding to input first data, determines the number of bits of a weight parameter according to required performance related to an inference to generate a second machine learning model, and acquires data output from the second machine learning model so as to correspond to the input first data with a smaller computational complexity than the first machine learning model.

Arithmetic operation device using a machine learning model, arithmetic operation method using a machine learning model, and training method of the machine learning model

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Kawasaki, Japan

Kengo Nakata