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Hyperspectral Data Compression Using Fully Convolutional autoencoder

REMOTE SENSING 2022年第10期14卷 2472页

作者： La Grassa, Riccardo Re, Cristina Cremonese, Gabriele Gallo, Ignazio Natl Inst Astrophys INAF I-35100 Padua Italy Univ Insubria Dept Theoret & Appl Sci I-21100 Varese Italy

In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step. Image compression covers a key role in space science and satellite imagery and, recently, deep learning models have achieved remarkable results in computer vision. In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet) and we conduct experiments over multi/hyperspectral and RGB datasets reporting improvements over all baselines used as benchmarks and than the JPEG family algorithm. Experimental results demonstrate the effectiveness in the compression ratio and spectral signal reconstruction and the robustness with a data type greater than 8 bits, clearly exhibiting better results using the PSNR, SSIM, and MS-SSIM evaluation criteria.

关键词： autoencoder data compression remote sensing satellite images

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Online Environment Abnormal Expression Detection Based on Improved autoencoder 19

Online Environment Abnormal Expression Detection Based on Im...

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Deng, Jinwei Wang, Xiaopu Zhang, Huiquan Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)9781665421744

Detecting the status of online learning and online working through human facial expressions and actions has been a hot topic in the field of Computer Vision in recent years. Anomalies have the characteristics of low occurrence probability, difficulty in definition and various types, so anomaly detection is extremely challenging. This paper is based on image rather than video to detect laptop users' abnormal expressions more quickly, which divided into three steps. Firstly, we use illumination compensation to improve the brightness of the original image, and then separate the face from the background by skin color segmentation. Secondly, we combine sparse coding and an additional encoder to extract face features based on autoencoder, which reduces the noise impact in the encoding process and enhances the robustness of the model. Finally, we put the extracted face features into one-class Support Vector Machine (SVM) classifier based on the radial basis function (RBF) for classification. This paper conducts experiments on three real datasets of learning, entertainment and working, which proves that the Fl- score of our proposed model is up to 0.89157.

关键词： anomaly detection skin color learning autoencoder one class Support Vector Machine(SVM) feature extraction

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Deep Convolutional Variational autoencoder for Anomalous Sound Detection 8

Deep Convolutional Variational Autoencoder for Anomalous Sou...

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8th IEEE International Conference on Communications and Electronics (IEEE ICCE)

作者： Minh-Hieu Nguyen Duy-Quang Nguyen Dinh-Quoc Nguyen Cong-Nguyen Pham Dai Bui Huy-Dung Han Hanoi Univ Sci & Technol Hanoi Vietnam Confluent Inc Mountain View CA USA

ISBN: (数字)9781728154718

ISBN: (纸本)9781728154718

Anomalous sound detection (ASD) is one of the most important fields in industrial facility maintenance. For this task, semi-supervised approaches are preferred thanks to their simplicity and no training data labels required. These methods train an autoencoder (AE) with only normal sound data and detect anomalies based on anomaly scores of actual samples. In this paper, we propose applying the convolutional variational autoencoder (CVAE) to ASD task. Through experiments using machine sound data, the CVAE is proven to be effective in detecting abnormal sound and outperform existing methods.

关键词： anomalous sound detection machine sound monitoring semi-supervised learning autoencoder

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An autoencoder for heterotic orbifolds with arbitrary geometry

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JOURNAL OF PHYSICS COMMUNICATIONS 2024年第2期8卷 025003页

作者： Escalante-Notario, Enrique Portillo-Castillo, Ignacio Ramos-Sanchez, Saul Univ Nacl Autonoma Mexico Inst Fis POB 20-364 Cdmx Mexico Univ Autonoma Chihuahua Fac Ingn Nuevo Campus Univ Nuevo Laredo 31125 Mexico

Artificial neural networks can be an important tool to improve the search for admissible string compactifications and characterize them. In this paper we construct the heterotic orbiencoder, a general deep autoencoder to study heterotic orbifold models arising from various Abelian orbifold geometries. Our neural network can be easily trained to successfully encode the large parameter space of many orbifold geometries simultaneously, independently of the statistical dissimilarities of their training features. In particular, we show that our autoencoder is capable of compressing with good accuracy the large parameter space of two promising orbifold geometries in just three parameters. Further, most orbifold models with phenomenologically appealing features appear in bounded regions of this small space. Our results hint towards a possible simplification of the classification of (promising) heterotic orbifold models.

关键词： autoencoder orbifold models string theory

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Convolutional autoencoder based Deep Learning Approach for Alzheimer's Disease Diagnosis using Brain MRI 34

Convolutional Autoencoder based Deep Learning Approach for A...

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34th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)

作者： Yagis, Ekin De Herrera, Alba G. Seco Citi, Luca Univ Essex Comp Sci & Elect Engn CSEE Colchester Essex England

ISBN: (纸本)9781665441216

Rapid and accurate diagnosis of Alzheimer's disease (AD) is critical for patient treatment, especially in the early stages of the disease. While computer-assisted diagnosis based on neuroimaging holds vast potential for helping clinicians detect disease sooner, there are still some technical hurdles to overcome. This study presents an end-to-end disease detection approach using convolutional autoencoders by integrating supervised prediction and unsupervised representation. The 2D neural network is based upon a pre-trained 2D convolutional autoencoder to capture latent representations in structural brain magnetic resonance imaging (MRI) scans. Experiments on the OASIS brain MRI dataset revealed that the model outperforms a number of traditional classifiers in terms of accuracy using a single slice.

关键词： Alzheimer's Disease Deep Learning Image Classification autoencoder MRI Neuroimaging

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Membership-Mappings for Data Representation Learning: A Bregman Divergence Based Conditionally Deep autoencoder 32nd

Membership-Mappings for Data Representation Learning: A Breg...

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32nd International Conference on Database and Expert Systems Applications (DEXA)

作者： Kumar, Mohit Moser, Bernhard Fischer, Lukas Freudenthaler, Bernhard Software Competence Ctr Hagenberg GmbH Hagenberg Austria Univ Rostock Fac Comp Sci & Elect Engn Inst Automat Rostock Germany

ISBN: (纸本)9783030871017;9783030871000

This paper suggests to use membership-mapping as the building block of deep models. An alternative idea of deep autoencoder, referred to as Bregman Divergence Based Conditionally Deep autoencoder (that consists of layers such that each layer learns data representation at certain abstraction level through a membership-mappings based autoencoder), is presented. A multi-class classifier is presented that employs a parallel composition of conditionally deep autoencoders to learn data representation for each class. Experiments are provided to demonstrate the competitive performance of the proposed framework in classifying high-dimensional feature vectors and in rendering robustness to the classification.

关键词： autoencoder Bregman divergence Variational learning

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AEGAN: Generating imperceptible face synthesis via autoencoder-based generative adversarial network

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COMPUTER ANIMATION AND VIRTUAL WORLDS 2023年第3-4期34卷

作者： Che, Aolin Yang, Jing-Hua Guo, Cai Dai, Hong-Ning Xie, Haoran Li, Ping Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Hanshan Normal Univ Sch Comp & Informat Engn Chaozhou Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hong Kong Polytech Univ Sch Design Hong Kong Peoples R China

Face recognition (FR) systems based on convolutional neural networks have shown excellent performance in human face inference. However, some malicious users may exploit such powerful systems to identify others' face images disclosed by victims' social network accounts, consequently obtaining private information. To address this emerging issue, synthesizing face protection images with visual and protective effects is essential. However, existing face protection methods encounter three critical problems: poor visual effect, limited protective effect, and trade-off between visual and protective effects. To address these challenges, we propose a novel face protection approach in this article. Specifically, we design a generative adversarial network (GAN) framework with an autoencoder (AEGAN) as the generator to synthesize the protection images. It is worth noting that we introduce an interpolation upsampling module in the decoder in order to let the synthesized protection images evade recognition by powerful convolution-based FR systems. Furthermore, we introduce an attention module with a perceptual loss in AEGAN to enhance the visual effects of synthesized images by AEGAN. Extensive experiments have shown that AEGAN not only can maintain the comfortable visual quality of synthesized images but also prevent the recognition of commercial FR systems, including Baidu and iKLYTEK.

关键词： autoencoder face image protection face recognition multi-head attention

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Few Shot Feature Extraction Network Based on autoencoder 22

Few Shot Feature Extraction Network Based on Autoencoder

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Zhenhui Hu Jinlong Chen Minghao Yang Guilin University of Electronic Technology China Brain-Inspired Intelligence Research Center Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781450398343

Few Shot Object Detection is becoming a hot issue in the problem of deep learning object detection. Its main goal is to use very few samples for training and learning to obtain a model with good detection speed and accuracy. However, the most significant problem of few shot object detection detection is that the extraction of target feature information is insufficient and incomplete due to the sparse sample data. The solution to this problem in main methods is often to increase the number of samples through data enhancement, which can greatly enhance sample feature information. However, there is still the problem of inaccurate extraction of target feature information. This paper proposes a feature extraction network based on an autoencoder, that is, under the framework of autoEncode, refer to the network structure of Vgg-16 and integrate the SeNet channel attention mechanism to adjust the channel feature information weight parameters in the feature extraction network, and then design the similarity evaluation method between the sample input image and the reconstructed image based on the autoencoder is used to quantify the index representation feature extraction ability. Finally, the experiments show, compared with other main feature extraction algorithms, our method proposed in this paper has a better performance on the feature extraction ability index on the small-sample fine-grained dataset Oxford-102 Flower, and the speed reaches the level of main algorithm, which shows that the scheme proposed in this paper effectively improves the feature learning and extraction ability in the condition of FSOD.

关键词： FSOD attention mechanism autoencoder feature extraction network

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Permeability prediction of low-resolution porous media images using autoencoder-based convolutional neural network

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartC期208卷 109589-109589页

作者： Zhang, HouLin Yu, Hao Yuan, XinHeng Xu, HengYu Micheal, Marembo Zhang, JiaNing Shu, HongLin Wang, GaoCheng Wu, HengAn Univ Sci & Technol China CAS Key Lab Mech Behav & Design Mat Dept Modern Mech CAS Ctr Excellence Complex Syst Mech Hefei 230027 Peoples R China Petrochina Zhejiang Oilfield Co Hangzhou Peoples R China

Permeability prediction of porous media from numerical approaches is an important supplement for experimental measurements with the benefits of being more economical and efficient. However, the accuracy and reliability of traditional numerical approaches are strongly dependent on the high-resolution images of porous media, which greatly limits their implementation for engineering applications. Herein, a semi-supervised machine learning approach is proposed to predict the permeability of porous media from low-resolution images. This approach consists of an autoencoder (AE) module trained with unlabeled data to assist the backbone convolutional neural network (CNN) in the prediction by providing a mapping of the low-resolution porous media to high-resolution features. The low-resolution information from CNN trained with small amount of labeled data and high-resolution information from AE trained with larger amount of unlabeled data are comprehensively considered in this approach. The prediction performance of AE-CNN from low-resolution images is examined against the results from traditional approaches of CNN and lattice Boltzmann method (LBM) by the mean-square errors (MSE) and R-Squared (R2) calculations. Using 5-fold cross-validation method, the average value of R2 is 0.896 on the test dataset by AE-CNN, compared to 0.869 for the traditional CNN without the AE. The MSEs for AE-CNN are 0.022 and 0.064 on the training and test datasets respectively in the best-performance fold, while without AE, the MSEs for only CNN are 0.034 and 0.083 on the training and test datasets respectively in the best-performance fold, implying that AE modules can substantially improve the prediction performance from low-resolution images of porous media. As for the simulation results of LBM approach, its prediction reliability (average R2: 0.42;MSE: 0.37 and 0.36 in the best-performance fold) is extremely lower than that of CNN-based machine learning algorithms owing to huge numerical error

关键词： Permeability prediction Low-resolution porous media Machine learning Convolutional neural network autoencoder

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DEEPStack-RBP: Accurate identification of RNA-binding proteins based on autoencoder feature selection and deep stacking ensemble classifier

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KNOWLEDGE-BASED SYSTEMS 2022年第0期256卷

作者： Wei, Qinqin Zhang, Qingmei Gao, Hongli Song, Tao Salhi, Adil Yu, Bin Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Coll Informat Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China China Univ Petr Qingdao Software Inst Coll Comp Sci & Technol Qingdao 266580 Peoples R China King Abdullah Univ Sci & Technol KAUST Computat Biosci Res Ctr CBRC Thuwal 23955 Saudi Arabia Univ Sci & Technol China Sch Data Sci Hefei 230027 Peoples R China Key Lab Computat Sci & Applicat Hainan Prov Haikou 571158 Peoples R China

RNA-binding proteins (RBPs) are involved in a number of biological processes such as RNA synthesis, protein folding, alternative splicing, etc. Predicting RBPs can facilitate the discovery and treatment of human diseases, such as muscle atrophy, nervous system diseases, and cancer. However, there are still various challenges in identifying RBPs using experimental methods. Computational methods, and in particular Deep Learning, are being deployed to alleviate some of these challenges and provide new avenues of investigation in the field of RBPs prediction. Here, we propose DEEPStack-RBP, a novel RBPs prediction tool based on deep learning and ensemble learning. First, conjoint triad (CT), local descriptors (LD), pseudo amino acid composition (PseAAC), multivariate mutual information (MMI) and position specific scoring matrix-transition probability composition (PSSM-TPC) are applied to extract multiple features from the proteins. Subsequently, autoencoder (AE) is used to eliminate redundancy in features, and SMOTE-ENN is employed to balance the samples by minimizing the number difference between positive and negative cases. Finally, the stacked ensemble classifier composed of bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and support vector machine (SVM) is used for prediction. On the training dataset RBP9873, the ACC value of DEEPStack-RBP reaches 98.76% with a MCC value of 0.9508. For the three independent test datasets of Human, S. cerevisiae and A. thaliana, the accuracy of the model is 97.16%, 97.67% and 99.57% respectively, and the MCC is 0.9405, 0.9499 and 0.9906 respectively. These results show that DEEPStack-RBP can be used as a powerful tool for RBPs prediction.(c) 2022 Elsevier B.V. All rights reserved.

关键词： RNA-binding proteins Multi-information fusion Bidirectional long short-term memory Gated recurrent unit autoencoder Stacked ensemble classifier

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