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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是1291-1300 订阅

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Generative Modeling Based Manifold Learning for Adaptive Filtering Guidance 48

Generative Modeling Based Manifold Learning for Adaptive Fil...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Helwani, Karim Smaragdis, Paris Goodwin, Michael M. Amazon Web Services United States University of Illinois Urbana-Champaign United States

ISBN: (纸本)9781728163277

In most practical adaptive filtering problems, estimated filters are not arbitrary, but instead lie on a manifold that encapsulates characteristics of the problem at hand. Consequently, it is desirable to steer adaptation towards filters that lie on that manifold. In this paper, we propose a novel approach to learn the manifold of a set of impulse responses and subsequently employ that learned manifold in an adaptation algorithm for system identification. The presented approach is a practical adaptive filtering recipe for enforcing a data-driven search domain constraint, instead of using conventional constrained optimization methods. © 2023 IEEE.

关键词： adaptive filtering generative models Manifold learning variational autoencoder

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Monolith to Microservices: VAE-Based GNN Approach with Duplication Consideration 16

Monolith to Microservices: VAE-Based GNN Approach with Dupli...

引用

16th IEEE International Conference on Service-Oriented System Engineering (SOSE)

作者： Sooksatra, Korn Maharjan, Rokin Cerny, Tomas Baylor Univ Dept Comp Sci Waco TX 76798 USA

ISBN: (数字)9781665475341

ISBN: (纸本)9781665475341

With the rise of cloud computing, many applications have been implemented into microservices to fully utilize cloud computing for scalability and maintainability purposes. However, there are some traditional monolith applications that developers would like to partition into microservices. Unfortunately, it is difficult to find a solution when considering multiple factors (i.e., the strong dependency in each cluster and how often different microservices communicate with each other). Further, because we allow duplications of classes in multiple microservices to reduce the communications between them, the number of duplicated classes is also another important factor for maintainability. Therefore, we need to use machine learning algorithms to approximate a good solution due to the infeasibility of finding the optimal solution. We apply the variational autoencoder to extract features of classes and use the fuzzy c means to group the classes into microservices according to their extracted features. As a result, our approach outperforms the other baselines in some significant metrics. Also, when we allow duplication, we find that it is helpful in terms of reducing the overhead of communications between microservices.

关键词： Microservices Machine learning Clustering Graph neural networks variational autoencoder

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Using autoencoders to Generate Skeleton-Based Typography 12th

Using Autoencoders to Generate Skeleton-Based Typography

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12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) Held as Part of EvoStar Conference

作者： Parente, Jessica Goncalo, Luis Martins, Tiago Cunha, Joao Miguel Bicker, Joao Machado, Penousal Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9783031299551;9783031299568

Type Design is a domain that multiple times has profited from the emergence of new tools and technologies. The transformation of type from physical to digital, the dissemination of font design software and the adoption of web typography make type design better known and more accessible. This domain has received an even greater push with the increasing adoption of generative tools to create more diverse and experimental fonts. Nowadays, with the application of Machine Learning to various domains, typography has also been influenced by it. In this work, we produce a dataset by extracting letter skeletons from a collection of existing fonts. Then we trained a variational autoencoder and a Sketch Decoder to learn to create these skeletons that can be used to generate new ones by exploring the latent space. This process also allows us to control the style of the resulting skeletons and interpolate between different characters. Finally, we developed new glyphs by filling the generated skeletons based on the original letters' stroke width and showing some applications of the results.

关键词： Type Design variational autoencoder Skeleton-basis Typography

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Generating Musical Sequences with a Given Emotion

Generating Musical Sequences with a Given Emotion

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Grekow, Jacek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

ISBN: (纸本)9781665442077

This article presents the process of building a system generating music content of a specified emotion. As the emotion labels, four basic emotions: happy, angry, sad, relaxed, which correspond to the four quarters of Russell's model, were used. Conditional variational autoencoder using a recurrent neural network for sequence processing was used as a generative model. The obtained results in the form of the generated music examples with a specific emotion are convincing in their structure and sound. The generated examples were evaluated through comparison with the training set.

关键词： Generative models music generation music emotion variational autoencoder

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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...

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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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DVAE-SR: Denoiser variational Auto-Encoder and Super-Resolution to Counter Adversarial Attacks 13

DVAE-SR: Denoiser Variational Auto-Encoder and Super-Resolut...

引用

13th International Conference on Machine Vision

作者： Dardour, Omar Zaied, Mourad Radeva, Petia Univ Gabes Zrig Eddakhlania Tunisia Univ Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781510640412

Recently, adversarial examples become one of the most dangerous risks in deep learning, which affects applications of real world such as robotics, cyber-security and computer vision. In image classification, adversarial attacks showed the ability to fool classifiers with small imperceptible perturbations added to the input. In this paper, we present an efficient defense mechanism, we call DVAE-SR that combine variational autoencoder and super-resolution to eliminate adversarial perturbation from image input before feeding it to the CNN classifier. The DVAE-SR can successfully defend against both white-box and black-box attacks without retraining CNN classifier and it recovers better accuracy than Defense-GAN and Defense-VAE..

关键词： Adversarial attacks Adversarial defense variational autoencoder Super-resolution Deep learning Image classification Computer vision

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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...

引用

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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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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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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3D FACIAL EXPRESSION GENERATOR BASED ON TRANSFORMER VAE 30

3D FACIAL EXPRESSION GENERATOR BASED ON TRANSFORMER VAE

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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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