Learning system and learning method

Abstract

The present invention provides a learning system that updates a parameter for a neural network with high speed and a learning method. The learning system includes a plurality of differential value calculators and a parameter update module to update the parameter for the neural network, wherein the plurality of differential value calculators execute following motions in a mutually unsynchronized manner: receiving a parameter of a certain time point from the parameter update module; calculating a differential value for updating the parameter according to the received parameter; and sending the differential value to the parameter update module. The parameter update module executes following motions: receiving the differential values from the differential value calculators; calculating the differential values with the plurality of differential value calculators, and updating the parameter according to the received differential values in an unsynchronized manner; and sending the updated parameter to the plurality of differential value calculators. The differential value calculators consider the passed time corresponding to the updating frequency during calculation of the differential values, and the updating frequency refers to the updated frequency of the parameter in a period from the start of a receiving time point of the parameter until the calculated differential values are used for updating the parameter by the parameter update module.

Description

technical field [0001] The invention relates to a learning system and a learning method for updating parameters used in a neural network. Background technique [0002] In the field of image recognition there is a problem known as general object recognition. This problem is a problem of estimating the type (class) of objects such as birds and cars existing in an image. In recent years, the recognition performance of the general object recognition problem has been significantly improved, which is largely brought about by the convolutional neural network (Convolution Neural Network, hereinafter referred to as CNN; for example, refer to Non-Patent Document 1). [0003] In the field of image recognition, various recognition algorithms have been proposed in the past, but as the learning data (a combination of input data and correct answers) becomes larger, CNN tends to exceed the recognition performance of other algorithms. CNN's model has a high expressive ability, but on the o...

AI Technical Summary

Problems solved by technology

CNN's model has a high performance ability, but on the other hand, it has been pointed out that CNN has a problem called "over-learning" because of the excessive specialization of the characteristics of the learning data.

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