Tag：Noise
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Time：2021-8-31
Author | Orhan Gazi YAL ç ı nvCompile VKSource: towards Data Science You may be familiar with different neural network structures. You may have heard of feedforward neural networks, CNN, RNNs. These neural networks are very useful for solving supervised learning tasks such as regression and classification. However, in the field of unsupervised learning, we […]
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Time：2021-8-24
This article is reproduced as follows: https://zhuanlan.zhihu.com/p/… Original text: deep residual shrink networks for fault diagnosis Authors: Minghang Zhao, Shisheng Zhong, Xuyun Fu Time: September 2019 1. Introduction In order to solve this problem, this paper proposes a residual shrinkage network, which adapts to determine the soft threshold through machine learning to eliminate the influence […]
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Time：2021-7-27
There are no dry goods, but mainly some ideas. I say you realize that there are 100 Hamlets in the eyes of 100 people. Isn’t this the case with academic exchanges? It’s easy to get started at the front end, but it’s harder to get to the back Why? Too much noise! What should I […]
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Time：2021-7-9
Introduction:In recent years, with the development of real-time communication technology, online meeting has gradually become an indispensable and important office tool in people’s work. According to incomplete statistics, about 75% of online meetings are pure voice meetings, that is, there is no need to turn on the camera and screen sharing function. At this time, […]
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Time：2021-5-13
abstractObjective: to introduce the frontier method of learning with noise, and to solve the optimization strategy of neural network in imperfect scenes, in order to improve the performance of the model. This article is from learning from noise labels with deep neural networks, Huawei cloud community, written by guessing ditch. Introduction： The success of neural […]
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Time：2021-5-11
Time series data are vulnerable to interferenceDisturbance of noiseTherefore, the ability to process the noise in time series will have an important impact on the classification accuracy [1]. Residual shrinkage network [2] is a classification algorithm for strong noise and high redundancy data, which was first proposed by M. Zhao in reference [2]. [1] Zhang […]
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Time：2021-2-26
1 introduction of FPN noise FPN (fixed pattern noise) is short for fixed pattern noise. According to the formation mechanism of FPN noise, it is divided into row FPN and column FPN. Line FPN: in tdi-cmos image sensor based on analog domain accumulation. Due to the parasitic resistance and capacitance in the analog accumulator circuit, […]
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Time：2021-2-19
1. Overview of denoising In noise removal, we should first recognize the types and formation of noise, and then de-noising should be carried out according to the characteristics of noise. Firstly, the noise types are as follows: Common names of noise Distribution type Cause of formation attribute Denoising method Shot noise Poisson distribution Ambient brightness […]
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Time：2021-2-17
RESNET, a residual network, has won the best paper award of IEEE Conference on computer vision and pattern recognition in 2016, with 38295 academic citations in Google. Deep residual shrinkage network is an improved version of deep residual network, which is actually the integration of deep residual network, attention mechanism and soft threshold function. To […]
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Time：2021-2-10
Can deep learning be used for pattern recognition of time series? For example, mechanical fault pattern recognition based on noise signal, have you done this research? -The answer of the great distance – Zhihu https://www.zhihu.com/question/40992219/answer/1325531901 Deep residual shrinkage network is a kind of mechanical fault pattern recognition method for noise and vibration signals. As shown […]
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Time：2020-12-7
RESNET, the residual network, won the best paper award of IEEE Conference on computer vision and pattern recognition in 2016, and has been cited 38295 times in Google. Deep residual shrinkage network is an improved version of deep residual network, which is actually the integration of deep residual network, attention mechanism and soft threshold function. […]
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Time：2020-11-30
Can deep learning be used in pattern recognition for time series? For example, is there any research on mechanical fault pattern recognition based on noise signal? -The answer of Yuncheng Wanlihttps://www.zhihu.com/question/40992219/answer/1325531901 The deep residual shrinkage network is a kind of mechanical fault pattern recognition method specially for noise and vibration signals. As shown in the […]
