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检索条件"主题词=variational autoencoder"

共 1537 条记录，以下是1301-1310 订阅

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On Learning-based Model for Dynamic Granular Prediction of Power Outages under Extreme Events

On Learning-based Model for Dynamic Granular Prediction of P...

引用

IEEE PES Conference on Innovative Smart Grid Technologies Latin America (ISGT-LA)

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

ISBN: (纸本)9798350336962

As the trend of climate change continues, an increase in the severity of extreme weather events is expected, posing a significant threat to the electric power infrastructure. The efficiency of service restoration efforts can be enhanced by having access to a highly granular outage forecasting tool with long lead times. In this study, we propose to develop and implement a multi-model framework as an operational tool that utilizes a dynamic, granular, multi-day outage forecasting model based on operational weather forecasts and detailed component outage information. To address the uneven distribution of different types of weather events and make better use of the timeseries data, a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework was developed to sample synthetic data and address data imbalance. With the balanced data, a prediction model was developed to estimate outages given a period of weather forecasts. The performance of the framework is demonstrated through several comparative studies.

关键词： variational autoencoder outage prediction recurrent neural networks long-short-term-memory weatherrelated outages

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Learning Traffic as Videos: A Spatio-Temporal VAE Approach for Traffic Data Imputation 30th

Learning Traffic as Videos: A Spatio-Temporal VAE Approach f...

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30th International Conference on Artificial Neural Networks (ICANN)

作者： Chen, Jiayuan Zhang, Shuo Chen, Xiaofei Jiang, Qiao Huang, Hejiao Gu, Chonglin Harbin Inst Technol Shenzhen Dept Comp Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9783030863838;9783030863821

In the real world, data missing is inevitable in traffic data collection due to detector failures or signal interference. However, missing traffic data imputation is non-trivial since traffic data usually contains both temporal and spatial characteristics with inherent complex relations. In each time interval, the traffic measurements collected in all spatial regions can be regarded as an image with more or fewer channels. Therefore, the traffic raster data over time can be learned as videos. In this paper, we propose a novel unsupervised generative neural network for traffic raster data imputation called STVAE, which works well robustly even under different missing rates. The core idea of our model is to discover more complex spatio-temporal representations inside the traffic data under the architecture of variational autoencoder (VAE) with Sylvester normalizing flows (SNFs). After transforming the traffic raster data into multi-channel videos, a Detection-and-Calibration Block (DCB), which extends 3D gated convolution and multi-attention mechanism, is proposed to sense, extract and calibrate more flexible and accurate spatio-temporal dependencies of the original data. The experiments are employed on three real-world traffic flow datasets and demonstrate that our network STVAE achieves the lowest imputation errors and outperforms state-of-the-art traffic data imputation models.

关键词： Traffic data imputation Unsupervised learning variational autoencoder Normalizing flows Attention mechanism 3D gated convolution

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3D FACIAL EXPRESSION GENERATOR BASED ON TRANSFORMER VAE 30

3D FACIAL EXPRESSION GENERATOR BASED ON TRANSFORMER VAE

引用

30th IEEE International Conference on Image Processing (ICIP)

作者： Zou, Kaifeng Yu, Boyang Seo, Hyewon Univ Strasbourg CNRS ICube Lab Strasbourg France

ISBN: (纸本)9781728198354

We present a generative model for the 3D facial expression mesh sequences, from onset to the termination of a desired expression. We tailor a Transformer VAE architecture: The encoder compresses a sequence of facial landmarks into an expression-aware regularized latent space, while the decoder generates a new sequence from the sampled latent variable, conditioned on a desired expression. After a landmark-guided mesh deformation, a given 3D neutral face is driven to an animated mesh sequence with the expected expression. The generated sequences are consistent, of quality, and exhibit a good level of diversity, improving over state-of-the-art methods. We validate our model by conducting extensive experiments on two representative datasets. The supplementary video and code are available on a GitHub page (https://***/ZOUKaifeng/ FacialExpressionGeneration).

关键词： transformer variational autoencoder facial expression generation

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DISENTANGLED SEQUENTIAL autoencoder WITH LOCAL CONSISTENCY FOR INFECTIOUS KERATITIS DIAGNOSIS 29

DISENTANGLED SEQUENTIAL AUTOENCODER WITH LOCAL CONSISTENCY F...

引用

IEEE International Conference on Image Processing (ICIP)

作者： Si, Yuxuan Fang, Zhengqing Kuang, Kun Huang, Zhengxing Yao, Yu-Feng Wu, Fei Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Zhejiang Univ Sir Run Run Shaw Hosp Sch Med Hangzhou 310027 Peoples R China Key Lab Corneal Dis Res Zhejiang Prov Hangzhou 310027 Peoples R China Zhejiang Univ Shanghai Inst Adv Study Shanghai 201203 Peoples R China Shanghai AI Lab Shanghai 201203 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Infectious keratitis is a major cause of visual impairment and a common blinding eye disease. Deep learning based prior researches mainly regard infectious keratitis diagnosis as a classification task on the slit-lamp images of single-visit. However, in real clinical applications, it is critical to analyze the lesion evolution characteristics represented by time-varying features over multiple-visits. To bridge this gap, in this paper, we focus on the problem with sequential clinical images of patients, and propose a novel disentangled sequential auto-encoder (DSLC-VAE) algorithm to separate the time-varying pathological features from the time-invariant ones for infectious keratitis diagnosis. Specifically, a inference model is exploited to generate time series of the shape and appearance of corneal lesions to represent keratitis progression, which are combined with location-related features to identify keratitis pathogen. Moreover, we construct a local consistent regularizer with a self-supervised task to enhance the consistency of the time-varying features across different infectious keratitis. Extensive experiments on real world dataset demonstrate superiority of our DSLC-VAE on both representation disentanglement and diagnosis accuracy.

关键词： Disentangled Representation Learning Sequential Learning variational autoencoder Local Consistency Constraint Infectious Keratitis

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Design of anomaly detection research model for multi-dimensional temporal data 4

Design of anomaly detection research model for multi-dimensi...

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4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

作者： Wang, Shuai Jiang, Yan Shenyang Univ Technol Sch Software Shenyang Liaoning Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In the production and daily life of various industries in today's society, it is often necessary to arrange a large number of sensors to collect data on a scheduled basis. However, due to factors such as collection errors, sensor failures, network transmission anomalies, and human influence, the obtained multidimensional temporal data may exhibit anomalies. In order to better analyze multidimensional temporal data, understand patterns, trends, and correlations in observed phenomena, and help people better analyze problems and make decisions, a design scheme for anomaly detection models that have always been oriented towards multidimensional temporal data is proposed. This scheme utilizes Convolutional Neural Networks (CNN) to extract local features, Long Short Term Memory Networks (LSTM) to store and extract time-series data features, combined with attention mechanisms to enhance feature extraction capabilities, and uses variational autoencoder (VAE) for anomaly detection, significantly improving the accuracy and efficiency of detection. Through experimental verification, this method performs well in practical applications and has broad application prospects.

关键词： component: Multidimensional Temporal Data Ab normal Detection Convolutional Neural Networks Long Short-term Memory Network variational autoencoder

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Single Cell Multi-Modal Analysis Using scDMVAE with an Emphasis on SCoPE2 Technology

Single Cell Multi-Modal Analysis Using scDMVAE with an Empha...

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作者： Zheng, Yi University of California Santa Barbara

学位级别：Ph.D., Doctor of Philosophy

Effective multi-modal integration of single cell datasets is critical for uncovering the biological properties of cells from different molecular perspectives. However, this poses significant challenges, including how to preserve shared information and account for differences between differently distributed datasets, how to integrate datasets linked by different anchors (cells or features) and how to improve the quality of datasets for integration. In this dissertation, we introduce two novel models that address these challenges. First, we present scDMVAE, a neural network model that can capture both shared and data-specific aspects of datasets in a latent space. scDMVAE can handle both cell-linked and feature-linked datasets through its embedding learning and attention-based matching components, respectively. We demonstrate the effectiveness of scDMVAE on a cell-linked CITE-seq dataset to reveal different cell type relations between mRNA and protein, and on feature-linked SCoPE2 proteomics and scRNA-Seq mRNA human testis datasets to transfer labels from mRNA to protein. Additionally, we present PCRID, a principal curve based model that aligns the retention time of peptides to improve confidence estimates of peptide-spectrum-matches (PSMs) in SCoPE2 technology. PCRID outperforms existing models like DART-ID by handling non-linearities in retention time more effectively, increasing the identification rate of peptides by 154.53 % at a PEP threshold of 0.01 while controlling false discoveries. Together, these models represent significant advances in single cell data analysis and have broad applications across related fields.

关键词： Multi-modal data integration variational autoencoder Single-cell data integration Proteomics data CITE-seq dataset

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Active Patterns Perceived for Stochastic Video Prediction 22

Active Patterns Perceived for Stochastic Video Prediction

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30th ACM International Conference on Multimedia (MM)

作者： Xu, Yechao Sun, Zhengxing Li, Qian Sun, Yunhan Luo, Shoutong Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Hunan Peoples R China

ISBN: (纸本)9781450392037

Predicting future scenes based on historical frames is challenging, especially when it comes to the complex uncertainty in nature. We observe that there is a divergence between spatial-temporal variations of active patterns and non-active patterns in a video, where these patterns constitute visual content and the former ones implicate more violent movement. This divergence enables active patterns the higher potential to act with more severe future uncertainty. Meanwhile, the existence of non-active patterns provides an opportunity for machines to examine some underlying rules with a mutual constraint between non-active patterns and active patterns. In order to solve this divergence, we provide a method called active patterns-perceived stochastic video prediction (ASVP) which allows active patterns to be perceived by neural networks during training. Our method starts with separating active patterns along with non-active ones from a video. Then, both scene-based prediction and active pattern-perceived prediction are conducted to respectively capture the variations within the whole scene and active patterns. Specially for active pattern-perceived prediction, a conditional generative adversarial network (CGAN) is exploited to model active patterns as conditions, with a variational autoencoder (VAE) for predicting the complex dynamics of active patterns. Additionally, a mutual constraint is designed to improve the learning procedure for the network to better understand underlying interacting rules among these patterns. Extensive experiments are conducted on both KTH human action and BAIR action-free robot pushing datasets with comparison to state-of-the-art works. Experimental results demonstrate the competitive performance of the proposed method as we expected. The released code and models are at https://***/tolearnmuch/ASVP.

关键词： Video prediction Stochastic video prediction Conditional generative adversarial network variational autoencoder Active pattern mining

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A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion 22

A Preliminary Study of a Two-Stage Paradigm for Preserving S...

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Interspeech Conference

作者： Huang, Wen-Chin Kobayashi, Kazuhiro Peng, Yu-Huai Liu, Ching-Feng Tsao, Yu Wang, Hsin-Min Toda, Tomoki Nagoya Univ Nagoya Aichi Japan Acad Sinica Taipei Taiwan Chi Mei Hosp Tainan Taiwan

ISBN: (纸本)9781713836902

We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, but as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient. In light of this, we suggest a novel, two-stage approach for DVC, which is highly flexible in that no normal speech of the patient is required. First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality. We investigate several design options. Experimental evaluation results demonstrate the potential of our approach to improving the quality of the dysarthric speech while maintaining the speaker identity.

关键词： dysarthric voice conversion sequence-to-sequence modeling nonparallel voice conversion variational autoencoder

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A VAE Conversion Method for Private Data Linkage 26

A VAE Conversion Method for Private Data Linkage

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26th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC)

作者： Tai, Bo-Chen Li, Szu-Chuang Huang, Yennun Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Tamkang Univ Dept Informat Commun New Taipei Taiwan

ISBN: (纸本)9781665424769

Data linkage plays a crucial role in realizing big data's value but is often regarded as a threat to personal privacy. Regulations like GDPR requires users' consent on each specific use of data, which is not practical for data analyzers. In this study, we propose a way to address the problem by having a trustworthy third party collect data from two or more parties, then use the data to train one or more variational autoencoder (VAE) models to remove privacy and send them to the data providers. Using this model, the users express their consent to share data with a trustworthy party. The third party links data from various datasets together to build a variational autoencoder model that allows all parties to generate datasets with full attributes without revealing sensitive personal data. System architectures and machine learning accuracy of generated data sets are measured in this study.

关键词： data linkage synthetic data variational autoencoder

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Rate Controllable Learned Image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

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IEEE International Conference on Visual Communications and Image Processing (VCIP)

作者： Zhang, Saiping Wang, Luge Mao, Xionghui Yang, Fuzheng Wan, Shuai Xidian Univ Sch Telecommun Engn Xian Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose a rate controllable image compression framework, Rate Controllable variational autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the stateof-the-art rate controllable image compression networks.

关键词： Deep image compression rate control variational autoencoder rate-distortion

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