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Coupled Deep autoencoder for Single Image Super-Resolution

IEEE TRANSACTIONS ON CYBERNETICS 2017年第1期47卷 27-37页

作者： Zeng, Kun Yu, Jun Wang, Ruxin Li, Cuihua Tao, Dacheng Xiamen Univ Sch Informat Sci & Engn Dept Comp Sci Xiamen 361005 Peoples R China Hangzhou Dianzi Univ Minist Educ Sch Comp Sci Hangzhou 310018 Zhejiang Peoples R China Hangzhou Dianzi Univ Minist Educ Key Lab Complex Syst Modeling & Simulat Hangzhou 310018 Zhejiang Peoples R China Univ Technol Sydney Ctr Quantum Computat & Intelligent Syst Fac Engn & Informat Technol Ultimo NSW 2007 Australia

Sparse coding has been widely applied to learning-based single image super-resolution (SR) and has obtained promising performance by jointly learning effective representations for low-resolution (LR) and high-resolution (HR) image patch pairs. However, the resulting HR images often suffer from ringing, jaggy, and blurring artifacts due to the strong yet ad hoc assumptions that the LR image patch representation is equal to, is linear with, lies on a manifold similar to, or has the same support set as the corresponding HR image patch representation. Motivated by the success of deep learning, we develop a data-driven model coupled deep autoencoder (CDA) for single image SR. CDA is based on a new deep architecture and has high representational capability. CDA simultaneously learns the intrinsic representations of LR and HR image patches and a big-data-driven function that precisely maps these LR representations to their corresponding HR representations. Extensive experimentation demonstrates the superior effectiveness and efficiency of CDA for single image SR compared to other state-of-the-art methods on Set5 and Set14 datasets.

关键词： autoencoder deep learning single image super-resolution (SR)

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EANet: Multiscale autoencoder based edge attention network for fluid segmentation from SD-OCT images

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023年第3期33卷 909-927页

作者： Pappu, Geetha Pavani Tankala, Sreekar Talabhaktula, Krishna Biswal, Birendra Sankar Fdn Eye Hosp & Inst Dept CQI & Res Visakhapatnam India Gayatri Vidya Parishad Coll Engn A Ctr Med Imaging Studies Dept ECE Visakhapatnam India Sankar Fdn Eye Hosp & Inst Vitreo Retina Dept Visakhapatnam India

Macular edema is a retinal complication that occurs due to the presence of excess fluid between the retinal layers. This might lead to swelling in the retina and cause severe vision impairment if not detected in its early stages. This paper presents a robust Edge Attention network (EANet) for segmenting the different retinal fluids like Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigment Epithelial Detachment (PED) from the Spectral Domain - Optical Coherence Tomography (SD-OCT) images. The proposed method employs a novel image enhancement technique by filtering OCT images using a BM3D (Block Matching and 3D Filtering) filter followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and a linear filter based on multivariate Taylor series to acquire the edge maps of the OCT images. A novel autoencoder based multiscale attention mechanism is incorporated with EANet that feeds on both the OCT image and edge-enhanced OCT image at every level of the encoder. The proposed network, EANet, has been trained and tested for the segmentation of all three types of fluids on the RETOUCH challenge dataset, and the segmentation of the IRF on the OPTIMA challenge and DUKE DME datasets. The average dice coefficient of IRF, SRF, and PED for the RETOUCH dataset is 0.683, 0.873, and 0.756, respectively, whereas it is 0.805, 0.77, and 0.756 for Cirrus, Spectralis, and Topcon vendors, respectively. The proposed method outperformed all the teams that participated in the OPTIMA challenge on all types of vendor images in terms of dice coefficient. The average dice coefficients of IRF on the OPTIMA and DUKE DME datasets are 0.84 and 0.72, respectively.

关键词： autoencoder edge enhancement macular edema multiscale attention mechanism optical coherence tomography images retinal fluid segmentation

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A new investment method with autoencoder: Applications to crypto currencies

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 162卷 113730-113730页

作者： Nakano, Masafumi Takahashi, Akihiko GCI Asset Management Chiyoda Ku 10F Chiyoda First Bldg East3-8-1 Nishi Kanda Tokyo 1010065 Japan Univ Tokyo Fac Econ Bunkyo Ku 7-3-1 Hongo Tokyo 1130033 Japan

This paper proposes a novel approach to the portfolio management using an autoencoder. In particular, features learned by an autoencoder with ReLU are directly exploited to portfolio constructions. Since the autoencoder extracts characteristics of data through a non-linear activation function ReLU, its realization is generally difficult due to the non-linear transformation procedure. In the current paper, we solve this problem by taking full advantage of the similarity of ReLU and an option payoff. Especially, this paper shows that the features are successfully replicated by applying so-called dynamic delta hedging strategy. An out of sample simulation with crypto currency dataset shows the effectiveness of our proposed strategy. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： autoencoder Crypto currency Delta hedging Artificial neural network

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Model of MT and MST areas using an autoencoder

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2003年第1-4期322卷 531-545页

作者： Katayama, K Ando, M Horiguchi, T Tohoku Univ GSIS Dept Math & Comp Sci Sendai Miyagi 9808579 Japan Tohoku Univ Elect Commun Res Inst Sendai Miyagi 9808577 Japan

We propose a model for a system with middle temporal neurons and medial superior temporal (MST) neurons by using a three-layered autoencoder. Noise effect is taken into account by using the framework of statistical physics. We define a cost function of the autoencoder, from which a learning rule is derived by a gradient descent method, within a mean-field approximation. We find a pair of values of two noise levels at which a minimum value of the cost function is attained. We investigate response properties of the MST neurons to optical flows for various types of motion at the pair of optimal values of two noise levels. We obtain that the response properties of the MST neurons are similar to those obtained from neurophysiological experiments. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： neural network MT neurons MST neurons optical flow autoencoder learning mean-field approximation

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Pattern discovery in time series using autoencoder in comparison to nonlearning approaches

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INTEGRATED COMPUTER-AIDED ENGINEERING 2021年第3期28卷 237-256页

作者： Noering, Fabian Kai-Dietrich Schroeder, Yannik Jonas, Konstantin Klawonn, Frank Volkswagen AG Wolfsburg Germany Univ Potsdam Potsdam Germany Deutsch Bahn AG Berlin Germany Ostfalia Univ Appl Sci Dept Comp Sci Wolfenbuettel Germany Helmholtz Ctr Infect Res Braunschweig Germany

In technical systems the analysis of similar situations is a promising technique to gain information about the system's state, its health or wearing. Very often, situations cannot be defined but need to be discovered as recurrent patterns within time series data of the system under consideration. This paper addresses the assessment of different approaches to discover frequent variable-length patterns in time series. Because of the success of artificial neural networks (NN) in various research fields, a special issue of this work is the applicability of NNs to the problem of pattern discovery in time series. Therefore we applied and adapted a Convolutional autoencoder and compared it to classical nonlearning approaches based on Dynamic Time Warping, based on time series discretization as well as based on the Matrix Profile. These nonlearning approaches have also been adapted, to fulfill our requirements like the discovery of potentially time scaled patterns from noisy time series. We showed the performance (quality, computing time, effort of parametrization) of those approaches in an extensive test with synthetic data sets. Additionally the transferability to other data sets is tested by using real life vehicle data. We demonstrated the ability of Convolutional autoencoders to discover patterns in an unsupervised way. Furthermore the tests showed, that the autoencoder is able to discover patterns with a similar quality like classical nonlearning approaches.

关键词： Time series data mining pattern discovery motif discovery autoencoder unsupervised

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Personalized recommendation with knowledge graph via dual-autoencoder

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APPLIED INTELLIGENCE 2022年第6期52卷 6196-6207页

作者： Yang, Yang Zhu, Yi Li, Yun Yangzhou Univ Sch Informat Engn Yangzhou 225009 Jiangsu Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

In the past decades, personalized recommendation systems have attracted a vast amount of attention and researches from multiple disciplines. Recently, for the powerful ability of feature representation learning, deep neural networks have achieved sound performance in the recommendation. However, most of the existing deep recommendation approaches require a large number of labeled data, which is often expensive and labor-some in applications. Meanwhile, the side information of users and items that can extend the feature space effectively is usually scarce. To address these problems, we propose a Personalized Recommendation method, which extends items' feature representations with Knowledge Graph via dual-autoencoder (short for PRKG). More specifically, we first extract items' side information from open knowledge graph like DBpedia as items' feature extension. Secondly, we learn the low-dimensional representations of additional features collected from DBpedia via the autoencoder module and then integrate the processed features into the original feature space. Finally, the reconstructed features is incorporated into the semi-autoencoder for personalized recommendations. Extensive experiments conducted on several real-world datasets validate the effectiveness of our proposed methods compared to several state-of-the-art models.

关键词： Knowledge graph autoencoder Recommendation systems

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Enhancing water quality prediction with advanced machine learning techniques: An extreme gradient boosting model based on long short-term memory and autoencoder

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JOURNAL OF HYDROLOGY 2024年 644卷

作者： Zhang, Kai Wang, Xinyuan Liu, Tianlong Wei, Wenguang Zhang, Fengshan Huang, Mingzhi Liu, Hongbin Nanjing Forestry Univ Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Fo Nanjing 210037 Peoples R China Guangxi Univ Coll Light Ind & Food Engn Guangxi Key Lab Clean Pulp & Papermaking & Pollut Nanning 530004 Peoples R China Shandong Huatai Paper Co Ltd Dongying 257335 Peoples R China Shandong Yellow Triangle Biotechnol Ind Res Inst C Dongying 257399 Peoples R China Western Univ Dept Chem & Biochem Engn London ON N6A 3K7 Canada South China Normal Univ Guangdong Prov Engn Res Ctr Intelligent Low carbon Guangzhou 510006 Peoples R China Nanan SCNU Inst Green & Low carbon Res SCNU NANAN Green & Low carbon Innovat Ctr Quanzhou 362300 Peoples R China

Achieving carbon neutrality in the pulp and paper industry necessitates effectively recycling pulp and papermaking wastewater, where continuous monitoring of effluent quality indices is crucial. This study suggests a novel machine learning-based model named LSTMAE-XGBOOST that integrates the feature extraction capabilities of autoencoder, the sequential feature learning capabilities of LSTM, and the high prediction accuracy of XGBOOST. This model is capable of extracting the complex relationships, non-Gaussian characteristics, and time series features from the papermaking wastewater data, and it demonstrates superior predictive performance. Compared to traditional machine learning models, the proposed model exhibits higher prediction accuracy. Specifically, when contrasted with partial least squares regression, LSTMAE-XGBOOST achieves a 40% increase in R-2 and a 35% reduction in RMSE. Further comparative assessments against other machine learning-based hybrid models with similar structures confirm the superiority of integrating LSTM and XGBOOST within the hybrid model approach. This study contributes a compelling methodology for modeling effluent quality indices, offering significant implications for environmental management in the pulp and paper industry.

关键词： Pulp and papermaking wastewater Machine learning autoencoder LSTM XGBOOST LSTM XGBOOST

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Sahand: A Software Fault-Prediction Method Using autoencoder Neural Network and K-Means Algorithm

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2024年第2期40卷 229-243页

作者： Arasteh, Bahman Golshan, Sahar Shami, Shiva Kiani, Farzad Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Islamic Azad Univ Dept Software Engn Tabriz Branch Tabriz Iran Seraj Inst Dept Software Engn Tabriz Azerbaijan Iran Fatih Sultan Mehmet Vakif Univ Data Sci Applicat & Res Ctr VEBIM Istanbul Turkiye

Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study.

关键词： Software Fault Prediction Clustering autoencoder K-means Accuracy

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Deep manifold matrix factorization autoencoder using global connectivity for link prediction

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APPLIED INTELLIGENCE 2023年第21期53卷 25816-25835页

作者： Lin, Xinyi Chen, Xiaoyun Zheng, Zhiwen Fuzhou Univ Coll Math & Stat Fuzhou 350116 Peoples R China

Link prediction aims to predict missing links or eliminate spurious links by employing known complex network information. As an unsupervised linear feature representation method, matrix factorization (MF)-based autoencoder (AE) can project the high-dimensional data matrix into the low-dimensional latent space. However, most of the traditional link prediction methods based on MF or AE adopt shallow models and single adjacency matrices, which cannot adequately learn and represent network features and are susceptible to noise. In addition, because some methods require the input of symmetric data matrix, they can only be used in undirected networks. Therefore, we propose a deep manifold matrix factorization autoencoder model using global connectivity matrix, called DM-MFAE-G. The model utilizes PageRank algorithm to get the global connectivity matrix between nodes for the complex network. DM-MFAE-G performs deep matrix factorization on the local adjacency matrix and global connectivity matrix, respectively, to obtain global and local multi-layer feature representations, which contains the rich structural information. In this paper, the model is solved by alternating iterative optimization method, and the convergence of the algorithm is proved. Comprehensive experiments on different real networks demonstrate that the global connectivity matrix and manifold constraints introduced by DM-MFAE-G significantly improve the link prediction performance on directed and undirected networks.

关键词： Link prediction Graph representation learning Deep matrix factorization Manifold regularization autoencoder

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Synthesizing Talking Faces from Text and Audio: An autoencoder and Sequence-to-Sequence Convolutional Neural Network

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PATTERN RECOGNITION 2020年第0期102卷 107231-000页

作者： Liu, Na Zhou, Tao Ji, Yunfeng Zhao, Ziyi Wan, Lihong Univ Shanghai Sci & Technol Inst Machine Intelligence Shanghai Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Dept Automat Shanghai Peoples R China Netease Inc Hangzhou Peoples R China

Synthesizing talking face from text and audio is increasingly becoming a direction in human-machine and face-to-face interactions. Although progress has been made, several existing methods either have unsatisfactory co-articulation modeling effects or ignore relations between adjacent inputs. Moreover, some of these methods often train models on shaky head videos or utilize linear-based face parameterization strategies, which further decrease synthesized quality. To address the above issues, this study proposes a sequence-to-sequence convolutional neural network to automatically synthesize talking face video with accurate lip sync. First, an advanced landmark location pipeline is used to accurately locate the facial landmarks, which can effectively reduce landmark shake. Then, a part-based autoencoder is presented to encode face images into a low-dimensional space and obtain compact representations. A sequence-to-sequence network is also presented to encode the relation of neighboring frames with multiple loss functions, and talking faces are synthesized through a reconstruction strategy with a decoder. Experiments on two public audio-visual datasets and a new dataset called CCTV news demonstrate the effectiveness of the proposed method against other state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Convolutional neural network autoencoder Regression Face landmark Face tracking Lip sync Video Audio

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