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A Sensitive Long Short-Term Memory Method Based on Differentiation of Input Changes

A long-term and short-term memory, sensitive technology, applied in the direction of biological neural network model, calculation, neural architecture, etc., can solve problems such as inability to achieve real-time performance, and achieve the effect of improving real-time analysis, improving real-time performance, and increasing responsiveness.

Active Publication Date: 2022-07-29
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0003] The existing long-term short-term memory network still has a major problem, that is, it uses long-term short-term memory to improve the analysis ability of information in the long-term sequence of the entire video, but there is no response to short-term information at all. ability, which makes the existing long-short-term memory network can only be used for post-event analysis, and cannot achieve good real-time performance and recognition of micro-movements and other content

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  • A Sensitive Long Short-Term Memory Method Based on Differentiation of Input Changes

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Embodiment Construction

[0033] Below in conjunction with accompanying drawing, the technical scheme of the present invention is described in further detail:

[0034] The principle of the invention is as follows: the core of the LSTM neural network is to add a memory module to learn the current information and extract the information and laws associated with the data, so as to transmit the information. A neural unit of the LSTM neural network contains three structures: the input gate i t , the forget gate f t and the output gate o t , each step t and its corresponding input sequence are X={x 1 , x 2 ,...,x t }. In order to improve its ability to respond to short-time information, the present invention adds an input differential sequence similar to differential effect

[0035] The present invention is a neural unit of a long short-term memory network with increased information sensitivity. The status information of the previous node is obtained from the input terminal c t-1 Input, every time ...

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Abstract

The invention discloses a sensitive long-term and short-term memory method based on input variation differentiation. In order to improve the traditional LSTM neural network's ability to respond to short-term information, a neural unit of the long-term and short-term memory network with increased information sensitivity is added, which can It greatly increases its ability to respond to short-term information, improves the real-time performance of its application, and enables more complete real-time analysis, further analysis of micro-actions and other contents, and improves the application value.

Description

technical field [0001] The invention relates to the field of long and short-term memory networks, in particular to a sensitive long-term and short-term memory method based on input variation differentiation. Background technique [0002] Artificial intelligence is one of the three important disciplines in the 21st century, and it is an important support for national science, economy and people's livelihood. Among them, long short-term memory network (LSTM) is an important algorithm for memory-based recognition, which has been recognized in many aspects including semantics, actions, texts, etc., and has good value. [0003] The existing long-term and short-term memory network still has a major problem, that is, it adopts the method of long-term and short-term memory to improve the analysis ability of information in the long-term sequence of the entire video, but it does not respond to short-term information at all. This makes the existing long and short-term memory network o...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06V40/20
CPCG06N3/049G06V40/20
Inventor 胡凯郑翡夏旻翁理国张彦雯王文晋
Owner NANJING UNIV OF INFORMATION SCI & TECH
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