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Deep Belief Networks for Image Denoising
[article]
2014
arXiv
pre-print
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Deep Belief Networks which are hierarchical generative models are effective tools for feature representation and extraction. ...
In this paper, we propose a novel method for image denoising which relies on the DBNs' ability in feature representation. This work is based upon learning of the noise behavior. ...
Deep Belief Networks One of the main problems in training deep networks is how to initialize weights. It is difficult to optimize the weights in nonlinear Deep Networks with multiple hidden layers. ...
arXiv:1312.6158v2
fatcat:736mol5twjdrloa3gc23lvzy2i
Facial Expression Recognition Method Based on Stacked Denoising Autoencoders and Feature Reduction
2017
DEStech Transactions on Engineering and Technology Research
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Based on the deep learning theory, a novel facial expression recognition method, which utilizes both Principal Component Analysis (PCA) and stacked denoising autoencoders (SDAE), is proposed in this paper ...
accuracy than traditional non-deep learning based expression recognition methods. ...
In the same year, Liu P, et al. proposed the combination of deep belief networks and AdaBoost method for facial expression recognition [10] . ...
doi:10.12783/dtetr/iceta2016/6996
fatcat:izifhy6s35cwvm3r7cbb6bvhp4
Deep Learning for Image Denoising: A Survey
[article]
2018
arXiv
pre-print
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Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising. ...
In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. ...
Universal denoising networks [22] for image denoising and deep CNN denoiser prior to eliminate multicative noise [34] are also effective for image denoising. ...
arXiv:1810.05052v1
fatcat:wmfrfbta4jajrmdj54hwbeejue
Predicting distributions with Linearizing Belief Networks
[article]
2016
arXiv
pre-print
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Such networks are particularly relevant to inverse problems such as image prediction for denoising, or text to speech. ...
Conditional belief networks introduce stochastic binary variables in neural networks. ...
ACKNOWLEDGEMENTS The authors would like to thank Marc'Aurelio Ranzato for insightful comments and discussions. ...
arXiv:1511.05622v4
fatcat:ta22g5y34zdf5f56ov622ymkoa
Medical Image Denoising Using Convolutional Denoising Autoencoders
2016
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)
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2016 pre-print arXiv:1608.04667v1 fatcat:wvr2o35idvfknltn77kryvc7baOther Versions
Heterogeneous images can be combined to boost sample size for increased denoising performance. ...
In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. ...
Which is in contrary to the belief that very large training datasets are needed for training deep architectures for good performance. ...
doi:10.1109/icdmw.2016.0041
dblp:conf/icdm/Gondara16
fatcat:wcrnxjc2m5elbopqjhxbmaqycm
Deep Learning for Computer Vision: A Brief Review
2018
Computational Intelligence and Neuroscience
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Belief Networks, and Stacked Denoising Autoencoders. ...
This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep ...
Deep Belief Networks and Deep Boltzmann Machines. ...
doi:10.1155/2018/7068349
pmid:29487619
pmcid:PMC5816885
fatcat:yeawpj32onfutegmkqpx4p6tsa
Two-Stage Approach to Image Classification by Deep Neural Networks
2018
EPJ Web of Conferences
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It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. ...
The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. ...
Deep and shallow Neural Networks Deep belief network. ...
doi:10.1051/epjconf/201817301009
fatcat:esfxfjstmnac5jrv7m3hrcbw24
Deep Learning in Character Recognition Considering Pattern Invariance Constraints
2015
International Journal of Intelligent Systems and Applications
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This research work reviews some of the most successful pre-training approaches to initializing deep networks such as stacked auto encoders, and deep belief networks based on achieved error rates. ...
Character recognition is a field of machine learning that has been under research for several decades. ...
the deep belief network. ...
doi:10.5815/ijisa.2015.07.01
fatcat:bwbts744lvfajjs7mptyvefyte
Learning structure in gene expression data using deep architectures, with an application to gene clustering
[article]
2015
bioRxiv
pre-print
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We propose that our deep architectures can be treated as empirical versions of Deep Belief Networks (DBNs). ...
In this paper, we use deep architectures pre-trained in an unsupervised manner using denoising autoencoders as a preprocessing step for a popular unsupervised learning task. ...
ACKNOWLEDGMENT AG is grateful to Volkan Cirik for valuable discussions on the capabilities of autoencoders. ...
doi:10.1101/031906
fatcat:4p6xmcqrbfec3h5g62klr3zkwu
Learning structure in gene expression data using deep architectures, with an application to gene clustering
2015
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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We propose that our deep architectures can be treated as empirical versions of Deep Belief Networks (DBNs). ...
In this paper, we use deep architectures pre-trained in an unsupervised manner using denoising autoencoders as a preprocessing step for a popular unsupervised learning task. ...
ACKNOWLEDGMENT AG is grateful to Volkan Cirik for valuable discussions on the capabilities of autoencoders. ...
doi:10.1109/bibm.2015.7359871
dblp:conf/bibm/GuptaWG15
fatcat:erhxclgfdng3hgy6pq4ootzb7m
Deep Learning on Image Denoising: An overview
[article]
2020
arXiv
pre-print
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We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy ...
However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. ...
Stacked auto-encoders (SARs) [80] and deep belief networks (DBNs) [17, 79] are typical deep neural networks. ...
arXiv:1912.13171v4
fatcat:4ts2xpivhreptelbgeqhljjiri
Deep Learning Techniques for Face Recognition: A Review
2019
International Journal for Research in Applied Science and Engineering Technology
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Deep learning techniques are becoming popular as it is able to handle large datasets. This paper provides an overview of deep learning techniques and its usage for face recognition. ...
It is a technology capable of identifying and verifying an individual from images and videos. ...
Deep Belief Networks Deep Belief Networks are one of the successful models of Deep learning. It consists of many layers of hidden units with a unsupervised learning algorithm. ...
doi:10.22214/ijraset.2019.6271
fatcat:ng6adarrwvbenlsphuffeovyzm
Multiscale residual fusion network for image denoising
2021
IET Image Processing
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Deep-learning methods have been developed in recent years and have achieved dramatic improvements for image denoising. ...
Experiments demonstrated that the MRF-Net outperformed several state-of-the-art model-based and deep-learning methods in both blind and non-blind image denoising tests. ...
A convolutional dictionary learning was employed for denoising by using some deep-learning-based image priors [13] . ...
doi:10.1049/ipr2.12394
fatcat:uly3js4tgbh7bascqtou4gfbdy
Deep networks for robust visual recognition
2010
International Conference on Machine Learning
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Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. ...
Recognition results after denoising are significantly better over the standard DBN implementations for various sources of noise. ...
Acknowledgements We thank the anonymous reviewers for making this a much better manuscript. This research was supported by NSERC. ...
dblp:conf/icml/TangE10
fatcat:xxncjb4amndi7pnwpdy4z6j4ra
A C++ library for Multimodal Deep Learning
[article]
2016
arXiv
pre-print
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MDL, Multimodal Deep Learning Library, is a deep learning framework that supports multiple models, and this document explains its philosophy and functionality. ...
For the last layer, compute δ
Figure 5 . 1 : 51 Deep Belief Network A Deep Belief Network (DBN) is a hybrid of a Restricted Boltzmann Machine and a Sigmoid Belief Network. ...
Training of the Deep Belief Network In training, the Deep Belief Network should maximize the likelihood of the training data. ...
arXiv:1512.06927v4
fatcat:2j2pt5x2wzgwpph7osd3xlyzha
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