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检索条件"主题词=Variational autoencoder"

共 1537 条记录，以下是461-470 订阅

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Stellar Cluster Detection using GMM with Deep variational autoencoder

Stellar Cluster Detection using GMM with Deep Variational Au...

引用

IEEE Conference on Recent Advances in Intelligent Computational Systems (RAICS)

作者： Karmakar, Arnab Mishra, Deepak Tej, Anandmayee Indian Inst Space Sci & Technol Dept Avion Trivandrum Kerala India Indian Inst Space Sci & Technol Dept Earth Ans Space Sci Trivandrum Kerala India

ISBN: (纸本)9781538673362

Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from the available image data. This paper describes an unsupervised approach in detecting star clusters using Deep variational autoencoder combined with a Gaussian Mixture Model. We show that our method works significantly well in comparison with state-of-the-art detection algorithm in recognizing a variety of star clusters even in the presence of noise and distortion.

关键词： Deep Learning variational autoencoder Gaussian Mixture Model Star Cluster Astronomy

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A Unified Unsupervised Gaussian Mixture variational autoencoder for High Dimensional Outlier Detection

A Unified Unsupervised Gaussian Mixture Variational Autoenco...

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IEEE International Conference on Big Data (Big Data)

作者： Liao, Weixian Guo, Yifan Chen, Xuhui Li, Pan Towson Univ Dept Comp & Informat Sci Towson MD 20252 USA Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA

ISBN: (纸本)9781538650356

Paradigm-shifting systems such as cyber-physical systems, collect data of high-or ultrahigh-dimensionality tremendously. Detecting outliers in this type of systems provides indicative understanding in wide-ranging domains such as system health monitoring, information security, etc. Previous dimensionality reduction based outlier detection methods suffer from the incapability of well preserving the critical information in the low-dimensional latent space, mainly because they generally assume an isotropic Gaussian distribution as prior and fail to mine the intrinsic multimodality in high dimensional data. Moreover, most of the schemes decouple the model learning process, resulting in suboptimal performance. To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture variational autoencoder for outlier detection. Specifically, a variational autoencoder firstly trains a generative distribution and extracts reconstruction based features. Then we adopt a deep brief network to estimate the component mixture probabilities by the latent distribution and extracted features, which is further used by the Gaussian mixture model to estimate sample densities with the Expectation-Maximization ( EM) algorithm. The inference model is optimized jointly with the variational autoencoder, the deep brief network, and the Gaussian mixture model. Afterwards, the proposed detector identifies outliers when the estimated sample density exceeds a learned threshold. Extensive simulations on six public benchmark datasets show that the proposed framework outperforms state-of-the-art outlier detection schemes and achieves, on average, 27% improvements in F1 score.

关键词： Outlier detection Gaussian mixture model variational autoencoder

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A Method to Inspect the Implementation of Electricity Price Based on Deep Learning variational autoencoder 2

A Method to Inspect the Implementation of Electricity Price ...

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2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Gao, Xiying Ye, Ning Song, Jinchun Wang, Haomiao Zhang, Ye Guan, Yan Song, Xiaowen Cai, Yingkai Zhang, Wenshu Hui, Qian Li, Dan State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Mkt Dept Shenyang Liaoning Peoples R China State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China State Grid Liaoyang Elect Power B&E Off Liaoyang Peoples R China

ISBN: (纸本)9781538685495

In this paper, we propose a method for performing electricity price execution inspection by using a variational autoencoder technology in deep learning. The variational autoencoder based anomaly detection algorithm(VABAD) can be used both as a discriminant model and as a feature of the generation model, which effectively solves the calculation problem of multiple heterogeneous parameters of current electricity price inspection implementation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. It is used by autoencoder based anomaly detection methods. Experimental results show that the proposed method has been validated and compared to the existing approaches. The databases used in this paper come from Power Marketing System that occurred in Liaoning, China in 2015.

关键词： deep learning variational autoencoder electricity price inspection implementation data heterogeneity

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Squeezed Convolutional variational autoencoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things

Squeezed Convolutional Variational AutoEncoder for Unsupervi...

引用

International Conference on Information and Computer Technologies (ICICT)

作者： Kim, Dohyung Yang, Hyochang Chung, Minki Cho, Sungzoon Kim, Huijung Kim, Minhee Kim, Kyungwon Kim, Eunseok Seoul Natl Univ Dept Ind Engn Data Min Ctr Seoul South Korea Samsung Elect Co Ltd Data Analyt Lab Software R&D Ctr Seoul South Korea

ISBN: (纸本)9781538653845

In this paper, we propose Squeezed Convolutional variational autoencoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.

关键词： edge computing IIoT unsupervised anomaly Detection manufacturing variational autoencoder VAE convolutional neural network CNN squeezenet

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STATISTICAL SPEECH ENHANCEMENT BASED ON PROBABILISTIC INTEGRATION OF variational autoencoder AND NON-NEGATIVE MATRIX FACTORIZATION

STATISTICAL SPEECH ENHANCEMENT BASED ON PROBABILISTIC INTEGR...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Bando, Yoshiaki Mimura, Masato Itoyama, Katsutoshi Yoshii, Kazuyoshi Kawahara, Tatsuya Kyoto Univ Grad Sch Informat Sakyo Ku Kyoto 6068501 Japan RIKEN Ctr Adv Intelligence Project Chuo Ku Tokyo 1030027 Japan

ISBN: (纸本)9781538646588

This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not robust against unknown environments. Another approach is to use non-negative matrix factorization (NMF) based on basis spectra trained on clean speech in advance and those adapted to noise on the fly. This semi-supervised approach, however, causes considerable signal distortion in enhanced speech due to the unrealistic assumption that speech spectrograms are linear combinations of the basis spectra. Replacing the poor linear generative model of clean speech in NMF with a VAE - a powerful nonlinear deep generative model trained on clean speech, we formulate a unified probabilistic generative model of noisy speech. Given noisy speech as observed data, we can sample clean speech from its posterior distribution. The proposed method outperformed the conventional DNN-based method in unseen noisy environments.

关键词： Single-channel speech enhancement variational autoencoder Bayesian signal processing

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Laser variational autoencoder for map construction and self-localization

Laser variational autoencoder for map construction and self-...

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

作者： Wakita, Shohei Nakamura, Takayuki Hachiya, Hirotaka Wakayama Univ Fac Syst Engn 930 Sakaedani Wakayama 6408510 Japan

ISBN: (纸本)9781538666500

For accurate global self-localization with small memory usage, researches for the compression of the laser-scan data have been actively conducted. Main approaches to the compression are to design feature extractor based on human knowledge regarding the specific environment, e.g., office and hallway. However, in real robot navigation tasks such as a security patrol robot, the robot would be applied to a variety of environments and it is expensive if the users need to tune the design at every environment. To alleviate such problem, we propose to extend the state-of-the-art variational auto-encoder (VAE) by introducing the step-edge detector, which detects non-continuous transition emerged frequently at the laser scan data due to the limitation of distance measurement. With our proposed method, called "laserVAE". the feature extractor of the laser scan is automatically tuned given unknown environments. Through experiments with a real self-localization with 2D laser scan, we demonstrate the effectiveness of the proposed method.

关键词： Feature description of scan data variational autoencoder Scan matching Global self-localization

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Learning Community Structure with variational autoencoder 18

Learning Community Structure with Variational Autoencoder

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Choong, Jun Jin Liu, Xin Murata, Tsuyoshi Tokyo Inst Technol Dept Comp Sci Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781538691595

Discovering community structure in networks remains a fundamentally challenging task. From scientific domains such as biology, chemistry and physics to social networks the challenge of identifying community structures in different kinds of network is challenging since there is no universal definition of community structure. Furthermore, with the surge of social networks, content information has played a pivotal role in defining community structure, demanding techniques beyond its traditional approach. Recently, network representation learning have shown tremendous promise. Leveraging on recent advances in deep learning, one can exploit deep learning's superiority to a network problem. Most predominantly, successes in supervised and semi-supervised task has shown promising results in network representation learning tasks such as link prediction and graph classification. However, much has yet to be explored in the literature of community detection which is an unsupervised learning task. This paper proposes a deep generative model for community detection and network generation. Empowered with Bayesian deep learning, deep generative models are capable of exploiting non-linearities while giving insights in terms of uncertainty. Hence, this paper proposes variational Graph autoencoder for Community Detection (VGAECD). Extensive experiment shows that it is capable of outperforming existing state-of-the-art methods. The generalization of the proposed model also allows the model to be considered as a graph generator. Additionally, unlike traditional methods, the proposed model does not require a predefined community structure definition. Instead, it assumes the existence of latent similarity between nodes and allows the model to find these similarities through an automatic model selection process. Optionally, it is capable of exploiting feature-rich information of a network such as node content, further increasing its performance.

关键词： community detection variational autoencoder generative model

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Linked Causal variational autoencoder for Inferring Paired Spillover Effects 18

Linked Causal Variational Autoencoder for Inferring Paired S...

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27th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Rakesh, Vineeth Guo, Ruocheng Moraffah, Raha Agarwal, Nitin Liu, Huan Arizona State Univ Tempe AZ 85287 USA Univ Arkansas Little Rock AR 72204 USA

ISBN: (纸本)9781450360142

Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. It helps us infer the causality between two seemingly unrelated set of events. For example, if consumer spending in the United States declines, it has spillover effects on economies that depend on the U.S. as their largest export market. In this paper, we aim to infer the causation that results in spillover effects between pairs of entities (or units);we call this effect as paired spillover. To achieve this, we leverage the recent developments in variational inference and deep learning techniques to propose a generative model called Linked Causal variational autoencoder (LCVA). Similar to variational autoencoders (VAE), LCVA incorporates an encoder neural network to learn the latent attributes and a decoder network to reconstruct the inputs. However, unlike VAE, LCVA treats the latent attributes as confounders that are assumed to affect both the treatment and the outcome of units. Specifically, given a pair of units u and (u) over bar, their individual treatment and outcomes, the encoder network of LCVA samples the confounders by conditioning on the observed covariates of u, the treatments of both u and u and the outcome of u. Once inferred, the latent attributes (or confounders) of u captures the spillover effect of (u) over bar on u. Using a network of users from job training dataset (LaLonde (1986)) and co-purchase dataset from Amazon e-commerce domain, we show that LCVA is significantly more robust than existing methods in capturing spillover effects.

关键词： causal inference spillover effect variational autoencoder

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Facial Image Inpainting with variational autoencoder

Facial Image Inpainting with Variational Autoencoder

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Ching-Ting Tu Yi-Fu Chen Department of Applied Mathematics National Chung Hsing University Taichung City Taiwan Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan

ISBN: (数字)9781728141923

ISBN: (纸本)9781728141930

This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area; for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard variational autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions; these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.

关键词： image inpainting variational autoencoder sampling Face Sampling Imagery (Psychotherapy) Image images Facies Beard Scarves decoders occluding

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Optimizing variational Graph autoencoder for Community Detection

Optimizing Variational Graph Autoencoder for Community Detec...

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IEEE International Conference on Big Data (Big Data)

作者： Choong, Jun Jin Liu, Xin Murata, Tsuyoshi Tokyo Inst Technol Dept Comp Sci Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781728108582

variational Graph autoencoders (VGAE) has recently been a popular framework of choice for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performances for challenging tasks such as link prediction, rating prediction and node clustering. However, a fundamental flaw exists in variational autoencoder (VAE) based approaches. Specifically, the objective function of VAE (reconstruction loss), deviates from its primary objective (i.e clustering). In this paper, we attempt to address this issue by introducing two significant changes to variational Graph autoencoder for Community Detection (VGAECD). Firstly, we introduce a simplified graph convolution encoder to increase convergence speed and reduce computational time. Secondly, a dual variational objective is introduced to encourage learning of the primary objective. The outcome is a faster converging model with competitive community detection performance.

关键词： community detection graph neural network variational autoencoder network embedding

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