A Two-Stage Unsupervised Deep Learning Framework for Degradation Removal in Ancient Documents

2021 ◽  
pp. 292-303
Author(s):  
Milad Omrani Tamrin ◽  
Mohammed El-Amine Ech-Cherif ◽  
Mohamed Cheriet
Keyword(s):  
Deep Learning ◽  
Two Stage ◽  
Learning Framework ◽  
Unsupervised Deep Learning ◽  
Ancient Documents

scholarly journals Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1040 ◽  
Author(s):  
Li Zhang ◽  
Xing Chen ◽  
Jun Yin
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Operating Characteristics ◽  
Learning Framework ◽  
Disease Similarity ◽  
Variational Autoencoder ◽  
Disease Associations ◽  
Unsupervised Deep Learning ◽  
Leave One Out

The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA). Through combining the integrated miRNA similarity and the integrated disease similarity with known miRNA–disease associations, respectively, we constructed two spliced matrices. These matrices were applied to train the variational autoencoder (VAE), respectively. The final predicted association scores between miRNAs and diseases were obtained by integrating the scores from the two trained VAE models. Unlike previous models, VAEMDA can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution. Compared with previous methods, VAEMDA obtained higher area under the receiver operating characteristics curves (AUCs) of 0.9118, 0.8652, and 0.9091 ± 0.0065 in global leave-one-out cross validation (LOOCV), local LOOCV, and five-fold cross validation, respectively. Further, the AUCs of VAEMDA were 0.8250 and 0.8237 in global leave-one-disease-out cross validation (LODOCV), and local LODOCV, respectively. In three different types of case studies on three important diseases, the results showed that most of the top 50 potentially associated miRNAs were verified by databases and the literature.

scholarly journals A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information

2018 ◽  
Vol 19 (2) ◽  
pp. 393-408 ◽  
Author(s):  
Yumeng Tao ◽  
Kuolin Hsu ◽  
Alexander Ihler ◽  
Xiaogang Gao ◽  
Soroosh Sorooshian
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Precipitation Amount ◽  
Skewed Distribution ◽  
Stage Model ◽  
Two Stage ◽  
Learning Framework ◽  
Second Stage ◽  
Satellite Information ◽  
Precipitation Estimation

Abstract Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms.

Stacked Convolutional Sparse Auto-Encoders for Representation Learning

2021 ◽  
Vol 15 (2) ◽  
pp. 1-21
Author(s):  
Yi Zhu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Representation Learning ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Models ◽  
Feature Representations ◽  
Learning Framework ◽  
Label Information ◽  
Unsupervised Deep Learning

Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods.

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