Deep learning internal data extraction method and device

Abstract

The invention discloses a data extraction method and device related to a deep learning model. According to a deep learning model of one embodiment, a data extraction method thereof comprises: a step of receiving an input query; a step of determining a first decision boundary set, the first decision boundary set being a subset of a decision boundary set corresponding to a target layer of the deep learning model; a step of extracting a decision region including the input query on the basis of the first set of decision boundaries; and a step of extracting data included in the decision region.

Description

technical field [0001] The following embodiments relate to data extraction methods and devices for deep learning models. Background technique [0002] Machine learning technology (machine learning) is an essential core technology in the era of big data (big data) or the Internet of things (IoT) that generate a large amount of data. Machine learning technology promises to have very large and wide-ranging ripple effects, as it can automate tasks in various fields that machines have not been able to do automatically. In particular, the rapid development of machine learning technology centered on deep learning has greatly narrowed the gap between the level required for practical applications and actual artificial intelligence technology, so it has attracted attention. [0003] Deep learning refers to machine learning techniques based on deep neural network models. In recent years, deep learning has played an important role in improving recognition performance in various fields...

AI Technical Summary

Problems solved by technology

However, due to the large number of nodes (hidden layers) and nonlinear functions (activation functions) that make up the deep learning model, there is a problem that it is difficult to explain how data is processed and output

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