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IEEE PHOTONICS JOURNAL 2024年第4期16卷 1页

作者： Lin, Jian Yan, Qiurong Zou, Quan Sun, Shida Wei, Zhen Du, Hua Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Northern Lianchuang Commun Co Ltd Doctoral Res & Innovat Ctr Nanchang 330001 Peoples R China Northern Lianchuang Commun Co Ltd Nanchang 330001 Peoples R China

Single-pixel imaging is an advanced computational imaging technique based on compressive sensing that offers higher signal-to-noise ratio and broader application scope compared to traditional imaging techniques. However, conventional reconstruction algorithms suffer from issues such as long processing time and low reconstruction accuracy during the sampling and reconstruction processes. Deep learning-based compressed reconstruction networks can circumvent the complex iterative computations of traditional algorithms and achieve fast, high-quality reconstruction. In this paper, we propose a variational Multi-Scale Error Compensation Network (VMSE) based on variational autoencoders. VMSE designs an error compensation network to enhance the feature representation capability of the sampling reconstruction network. We employ multiple latent variables to generate error features at different scales in the intermediate layers of the error compensation network, compensating the reconstructed image. Additionally, we design a module that simultaneously learns in the spatial and frequency domains, which is used for upsampling and complementing the missing high-frequency information in the frequency domain. On the MNIST dataset, when the sampling rate is 0.025, VMSE achieved higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) scores, especially with an SSIM score of 0.963, significantly surpassing Reconnet and DR2Net's scores of 0.930 and 0.920, respectively. This was further corroborated by practical experiments, where at low sampling rates, VMSE could reconstruct outlines more clearly compared to TVAL3.

关键词： Single pixel imaging deep learning variational autoencoder imaging reconstruction Single pixel imaging deep learning variational autoencoder imaging reconstruction

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variational data augmentation for a learning-based granular predictive model of

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Zhao, Tianqiao Yue, Meng Jensen, Michael Endo, Satoshi Marschilok, Amy C. Nugent, Brian Cerruti, Brian Spanos, Constantine Brookhaven Natl Lab Upton NY 11973 USA Orange & Rockland Util Inc Pearl River NY 10965 USA Con Edison Co New York NY USA

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. In this study, we propose to develop and implement a multi -model framework as an operational tool based on a granular and multi -day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time -series data. Case studies are performed to demonstrate the performance of the new framework.

关键词： variational data augmentation Outage prediction variational autoencoder Recurrent neural networks Weather-related outages

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An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期95卷

作者： Nagachandrika, Bathula Prasath, R. Joe, I. R. Praveen KCG Coll Technol Dept Comp Sci & Engn Chennai 600097 Tamil Nadu India Vellore Inst Technol Comp Sci & Engn Tiruvalam Rd Vellore 632014 Tamil Nadu India

This method of identifying plant leaf disease generally involves a large team of experts with extensive knowledge of plant diseases, and it can be expensive, time-consuming, and subjective. Hence, a novel plant leaf disease classification framework is proposed to classify the plant diseases and then take preventive measures based on the classified outcomes. The plant leaf images are collected from traditional databases. The classification of leaf diseases is done with the support of the developed Multi-scale Feature Fusion-based Adaptive Deep Network (MFF-ADNet). In this developed MFF-ADNet, two processes are carried out such as feature extraction and classification. The collected images are given to the feature extraction phase, where the Visual Geometry Group (16) (VGG16), variational autoencoder (VAE), and Visual Transformer (ViT) network are used for extracting the features. The extracted features are fused and the resultant Multi-scale fused features are provided to the input of the classification process. Here, the Adaptive Convolutional Neural Network with Attention Mechanism (CNNAM) is utilized for classifying the plant leaf diseases and the parameters are optimized using the Enhanced Gannet Optimization Algorithm (EGOA) approach. From the results, the median value is obtained for a proposed method that is more than 7.18% of MAO-MFF-ADNet, 4.11% of TSO-MFF-ADNet, 8.03% of CO-MFF-ADNet and 4.07% of GOA-MFF-ADNet. Therefore, the experimental outcome of the developed plant leaf classification model is validated over various approaches to ensure the goodness of the developed scheme.

关键词： Classification of Plant Leaf Diseases Multi-scale Feature Fusion-based Adaptive Deep Network Visual Geometry Group 16 variational autoencoder Visual Transformer Adaptive Convolutional Neural Network with Attention Mechanism Enhanced Gannet Optimization Algorithm

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variational autoencoders for Baseball Player Evaluation 5

Variational Autoencoders for Baseball Player Evaluation

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5th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Converse, Geoffrey Arnold, Brooke Curi, Mariana Oliveira, Suely Univ Iowa Iowa City IA 52242 USA Univ Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9781643680194;9781643680187

In the sporting world, baseball has been quicker to embrace the use of data analytics than any other sport, as detailed baseball statistics have become readily available in large and diverse quantities to the general public. Professional baseball teams use this data to develop game plans and evaluate players. In this work, we explore the latter by using a variational autoencoder (VAE), a special class of artificial neural networks. Specifically, we wish to relate a player's season-long batting statistics with the latent skills that a professional athlete needs to succeed in the MLB. In the growing field of sports analytics, we find this work incredibly important as it provides a novel, flexible, and powerful method to predict specific athletic skills based on years of recorded statistics.

关键词： Neural Networks variational autoencoder Interpretability Sports Analytics

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Anomaly Detection Using autoencoders for Movement Prediction 27th

Anomaly Detection Using Autoencoders for Movement Prediction

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27th Brazilian Congress on Biomedical Engineering (CBEB)

作者： Barbosa, L. J. L. Delis, A. L. Cotta, P. V. P. Silva, V. O. Araujo, M. D. C. Rocha, A. Univ Brasilia Engn Biomed Brasilia DF Brazil Med Biophys Ctr Santiago De Cuba Cuba

ISBN: (纸本)9783030706012;9783030706005

The smaller the time window, the faster the response of a prosthesis to the user's movement. However, very small windows have very little information, making it difficult to classify the surface electromyography signal (sEMG). This article presents the use of autoencoders for the detection of motion in real-time processing. For this purpose, a time window of 0.01 s window (i.e., ten samples per window). The difference between the number of peaks and the distance between them in the resulting latent space makes it possible to classify the moment when the patient starts to move. Through an autoencoder as an anomaly detector, it was possible to classify the beginning of the user's movement, thus managing to improve the classification in real-time.

关键词： EMG variational autoencoder Deep learning Information

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Hyperspectral Unmixing with Spectral Variability Using Endmember Guided Probabilistic Generative Deep Learning

Hyperspectral Unmixing with Spectral Variability Using Endme...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lyngdoh, Rosly B. Dave, Rucha Anand, S. S. Ahmad, Touseef Misra, Arundhati Space Applicat Ctr ISRO Ahmedabad Ahmadabad 380015 Gujarat India Anand Agr Univ Anand 388110 Gujarat India

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Spectral signatures of the pure constituent materials vary across the hyperspectral image (HSI) due to variable illumination, atmospheric conditions, and intrinsic variability. Using a single endmember to represent the target material (or endmember) with high spectral variability will lead to errors in estimating abundance. Therefore, we propose a probabilistic generative network (PGM-Net) architecture to learn the spectral variability from the HSI (hereinafter referred to as endmember-guided-probabilistic-model-network, EGPGM-Net). The PGMNet is guided by endmember-network (E-Net) using the parameter sharing strategy. Experimental analysis was carried out on benchmark datasets to compare the performance of the proposed method with the state-of-the-art methods. Moreover, we have also demonstrated the application of EGPGM-Net for estimating the abundance of red and black soil over sparsely vegetated areas using airborne-visible-and-infrared-imaging-spectrometer-next-generation (AVIRIS-NG) sensor. The quantitative analysis reveals that the proposed method consistently achieves a better unmixing performance than other linear-mixing and deep learning based models in terms of spectral-angle-distance (SAD) and abunance-root-mean-square error (aRMSE). The proposed semi-supervised approach accurately delineated the abundances of red soil, black soil, crop residue, built-up areas and bituminous roads.

关键词： Hyperspectral Image AVIRIS-NG Red Soil Black Soil variational autoencoder Unmixing

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RETINAL VESSEL SEGMENTATION WITH VAE RECONSTRUCTION AND MULTI-SCALE CONTEXT EXTRACTOR 19

RETINAL VESSEL SEGMENTATION WITH VAE RECONSTRUCTION AND MULT...

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19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI)

作者： Xu, Weijin Yang, Huihua Zhang, Mingying Pan, Xipeng Liu, Wentao Yan, Songlin Beijing Univ Posts & Telecommun Beijing Peoples R China China Elect Standardizat Inst Beijing Peoples R China Guilin Univ Elect Technol Guilin Peoples R China

ISBN: (纸本)9781665429238

The clinical diagnosis of eye disorders including diabetic retinopathy relies heavily on retinal vessel segmentation. CNN-based methods are the preferred approaches for retina vessel segmentation in recent years, but they are data hungry and prone to overfitting on the training set and achieving suboptimal results on the validation set or the test set. Taking this into consideration, we propose to integrate a variational autoencoder reconstruction branch to pose extra regularization on the shared encoder and increase the generalization ability of networks. Furthermore, to deal with the unbalanced vessel scale distribution, a multi-scale context extractor is carefully designed, which employed the regular convolution and dilated convolution to extract multi-scale context and utilized different fusion method to obtain better complementary features. Extensive experiment results demonstrate that our proposed method achieves comparable state-of-the-art performance on the popular datasets: DRIVE and CHASEDB1.

关键词： Retinal vessel segmentation convolutional neural network variational autoencoder multi-scale

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AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE 14

AS-IntroVAE: Adversarial Similarity Distance Makes Robust In...

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Asian Conference on Machine Learning (ACML)

作者： Lu, Changjie Zheng, Shen Wang, Zirui Dib, Omar Gupta, Gaurav Wenzhou Kean Univ Wenzhou Peoples R China Carnegie Mellon Univ Pittsburgh PA USA Zhejiang Univ Hangzhou Peoples R China

Recently, introspective models like IntroVAE and S-IntroVAE have excelled in image generation and reconstruction tasks. The principal characteristic of introspective models is the adversarial learning of VAE, where the encoder attempts to distinguish between the real and the fake (i.e., synthesized) images. However, due to the unavailability of an effective metric to evaluate the difference between the real and the fake images, the posterior collapse and the vanishing gradient problem still exist, reducing the fidelity of the synthesized images. In this paper, we propose a new variation of IntroVAE called Adversarial Similarity Distance Introspective variational autoencoder (AS-IntroVAE). We theoretically analyze the vanishing gradient problem and construct a new Adversarial Similarity Distance (AS-Distance) using the 2-Wasserstein distance and the kernel trick. With weight annealing on AS-Distance and KL-Divergence, the AS-IntroVAE are able to generate stable and high-quality images. The posterior collapse problem is addressed by making per-batch attempts to transform the image so that it better fits the prior distribution in the latent space. Compared with the per-image approach, this strategy fosters more diverse distributions in the latent space, allowing our model to produce images of great diversity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of AS-IntroVAE on image generation and reconstruction tasks.

关键词： Image Generation variational autoencoder Introspective Learning

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variational Inference via R′enyi Upper-Lower Bound Optimization 21

Variational Inference via R′enyi Upper-Lower Bound Optimiza...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Zalman, Dana Oshri Fine, Shai Reichman Univ Sch Comp Sci Herzliyya Israel

ISBN: (纸本)9781665462839

variational inference provides a way to approximate probability densities. It does so by optimizing an upper or a lower bound on the likelihood of the observed data (the evidence). The classic variational inference approach suggests to maximize the Evidence Lower BOund (ELBO). Recent proposals suggest to optimize the variational R ' enyi bound (VR) and. upper bound. However, these estimates are either biased or difficult to approximate, due to a high variance. In this paper we introduce a new upper bound (termed VRLU) which is based on the existing variational R ' enyi bound. In contrast to the existing VR bound, the Monte Carlo (MC) approximation of the VRLU bound is unbiased. Furthermore, we devise a (sandwiched) upper-lower bound variational inference method (termed VRS) to jointly optimize the upper and lower bounds. We present a set of experiments, designed to evaluate the new VRLU bound, and to compare the VRS method with the classic VAE and the VR methods over a set of digit recognition tasks. The experiments and results demonstrate the VRLU bound advantage, and the wide applicability of the VRS method.

关键词： variational autoencoder R ' enyi Divergence

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Two-Channel VAE-GAN Based Image-To-Video Translation 1

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18th International Conference on Intelligent Computing (ICIC)

作者： Wang, Shengli Xieshi, Mulin Zhou, Zhangpeng Zhang, Xiang Liu, Xujie Tang, Zeyi Dai, Yuxing Xu, Xuexin Lin, Pingyuan Maintenance Co State Grid Power Co Gansu Prov Lanzhou 730000 Gansu Peoples R China State Grid Infotelecom Great Power Sci & Technol Fuzhou 350000 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

ISBN: (数字)9783031138706

ISBN: (纸本)9783031138706;9783031138690

We propose a VAE-GAN network with a two-channel decoder for addressing multiple image-to-video translation tasks, i.e., generating multiple videos of different categories by a single model. We consider this image-to-video translation as a video generation task rather than a video prediction that needs multiple frames as input. After training, the model only requires the first frame of the video and its corresponding attribute to generate the required video. The advantage of combining the variational autoencoder (VAE) and Generative Adversarial Network (GAN) is to avoid the shortcomings of both: VAE components can give rise to blur, and unstable gradients caused by the GAN. Extensive qualitative and quantitative experiments are conducted on the MUG [1] dataset. We draw the following conclusions from this empirical study: compared with state-of-the-art approaches, our approach (VAE-GAN) exhibits significant improvements in generative capability.

关键词： Video generation variational autoencoder Generative adversarial network

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