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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Choudhury, Aritro Pal Nath Tripathy, Soumyo Mandal, Soumya Kanti Halder, Tamesh De, Shaunak Gayen, Rintu Kumar Chakravarty, Debashish Institute of Engineering & Management Department of Electronics and Communication Engineering Kolkata India Institute of Engineering & Management Computer Science Engineering Kolkata India Iit Kharagpur Ndl India Indian Institute of Technology Department of Mining Engineering Kharagpur India Capella Space San FranciscoCA United States

ISBN: (纸本)9798350348460

Capturing changes in certain areas has assumed huge importance due to both man-made and natural phenomenon. Our novel research is aimed at urban classification using an unsupervised variational Auto-encoder based Siamese network model for change detection on satellite imagery. It works well on Polarimetric Synthetic Aperture Radar (PolSAR) images of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) as well as optical satellite images. The model encodes the images to get the latent feature vectors, on which the Siamese difference is performed and the resultant latent space is decoded to get the difference image. The metrics which help us determine how well our model predicts the mask are the recall score and the receiver operating characteristic curve. We have archived a recall score of 0.87 and the area under the receiver operating characteristic curve is 0.98 © 2024 IEEE.

关键词： Change Detection Latent Features Receiver Operating Characteristic Curve Siamese Network unsupervised variational autoencoder

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mmFall: Fall Detection Using 4-D mmWave Radar and a Hybrid variational RNN autoencoder

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 1245-1257页

作者： Jin, Feng Sengupta, Arindam Cao, Siyang Univ Arizona Dept Elect & Comp Engn Tucson AZ 85719 USA

Elderly fall prevention and detection becomes extremely crucial with the fast aging population globally. In this article, we propose mmFall, a novel fall detection system, which comprises 1) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid and 2) a hybrid variational recurrent neural network (RNN) autoencoder (HVRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is detected when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor offers privacy-compliance and high sensitivity to motion, over the traditional sensing modalities. However, 1) randomness in radar point cloud and 2) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two major challenges. To overcome the randomness in radar data, the proposed HVRAE uses variational inference, a generative approach rather than a discriminative approach, to infer the posterior probability of the body's latent motion state every frame, followed by a RNN to summarize the temporal features over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the HVRAE is built upon an autoencoder architecture in a semisupervised approach, which is only trained on the normal activities of daily living (ADL). In the inference stage, the HVRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment,(1) we implemented the HVRAE along with two other baselines, and tested on the data set collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms baselines and achieves 98% detection out of 50 falls at the expense of just 2 false alarms. Note to Practitioners-Traditional nonwearable fall detection approaches typically make use of a vision-based sensor, such as camera, to mo

关键词： Radar Three-dimensional displays Radar detection Senior citizens Doppler radar Radar antennas Radar measurements Anomaly detection fall detection millimeter wave (mmWave) radar recurrent autoencoder (RAE) semisupervised learning variational autoencoder

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Curiosity-Driven variational autoencoder for Deep Q Network 24th

Curiosity-Driven Variational Autoencoder for Deep Q Network

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24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Han, Gao-Jie Zhang, Xiao-Fang Wang, Hao Mao, Chen-Guang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783030474263;9783030474256

In recent years, deep reinforcement learning (DRL) has achieved tremendous success in high-dimensional and large-scale space control and sequential decision-making tasks. However, the current model-free DRL methods suffer from low sample efficiency, which is a bottleneck that limits their performance. To alleviate this problem, some researchers used the generative model for modeling the environment. But the generative model may become inaccurate or even collapse if the state has not been sufficiently explored. In this paper, we introduce a model called Curiosity-driven variational autoencoder (CVAE), which combines variational autoencoder and curiosity-driven exploration. During the training process, the CVAE model can improve sample efficiency while curiosity-driven exploration can make sufficient exploration in a complex environment. Then, a CVAE-based algorithm is proposed, namely DQN-CVAE, that scales CVAE to higher dimensional environments. Finally, the performance of our algorithm is evaluated through several Atari 2600 games, and the experimental results show that the DQN-CVAE achieves better performance in terms of average reward per episode on these games.

关键词： Reinforcement learning Deep Q learning Exploration variational autoencoder

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Learning latent representations of bank customers with the variational autoencoder

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 164卷 114020-114020页

作者： Mancisidor, Rogelio A. Kampffmeyer, Michael Aas, Kjersti Jenssen, Robert UiT Arctic Univ Norway Fac Sci & Technol Dept Phys & Technol Hansine Hansens Veg 18 N-9037 Tromso Norway Santander Consumer Bank Credit Risk Models Strandveien 18 N-1325 Lysaker Norway Norwegian Comp Ctr POB 114 Blindern Oslo Norway UiT Machine Learning Grp Tromso Norway

Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we show that it is possible to steer data representations in the latent space of the variational autoencoder (VAE) using a semi-supervised learning framework and a specific grouping of the input data called Weight of Evidence (WoE). Our proposed method learns a latent representation of the data showing a well-defied clustering structure. The clustering structure captures the customers' creditworthiness, which is unknown a priori and cannot be identified in the input space. The main advantages of our proposed method are that it captures the natural clustering of the data, suggests the number of clusters, captures the spatial coherence of customers' creditworthiness, generates data representations of unseen customers and assign them to one of the existing clusters. Our empirical results, based on real data sets reflecting different market and economic conditions, show that none of the well-known data representation models in the benchmark analysis are able to obtain well-defined clustering structures like our proposed method. Further, we show how banks can use our proposed methodology to improve marketing campaigns and credit risk assessment.

关键词： variational autoencoder Data representations Clustering Machine learning

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Reduced-Complexity End-to-End variational autoencoder for on Board Satellite Image Compression

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REMOTE SENSING 2021年第3期13卷 447-447页

作者： Alves de Oliveira, Vinicius Chabert, Marie Oberlin, Thomas Poulliat, Charly Bruno, Mickael Latry, Christophe Carlavan, Mikael Henrot, Simon Falzon, Frederic Camarero, Roberto Univ Toulouse IRIT INP ENSEEIHT F-31071 Toulouse France Telecommun Space & Aeronaut TeSA Lab F-31500 Toulouse France Univ Toulouse ISAE SUPAERO F-31055 Toulouse France CNES F-31400 Toulouse France Thales Alenia Space F-06150 Cannes France ESA NL-2201 AZ Noordwijk Netherlands

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off;however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

关键词： remote sensing lossy compression on board compression transform coding rate-distortion JPEG2000 CCSDS learned compression neural networks variational autoencoder complexity

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UNSUPERVISED ADAPTATION FOR HIGH-DIMENSIONAL WITH LIMITED-SAMPLE DATA CLASSIFICATION USING variational autoencoder

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COMPUTING AND INFORMATICS 2021年第1期40卷 1-28页

作者： Mahmud, Mohammad Sultan Huang, Joshua Zhexue Fu, Xianghua Ruby, Rukhsana Wu, Kaishun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis.

关键词： HDLSS problem dimensionality reduction unsupervised framework variational autoencoder deep learning

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Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder

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COMPUTERS & GEOSCIENCES 2021年 152卷 104762-104762页

作者： Lopez-Alvis, Jorge Laloy, Eric Nguyen, Frederic Hermans, Thomas Univ Liege Urban & Environm Engn Appl Geophys Liege Belgium Univ Ghent Dept Geol Ghent Belgium Belgian Nucl Res Ctr Inst Environm Hlth & Safety Engn & Geosyst Anal Mol Belgium

When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e.g. the geological setting) of the subsurface. In such case, inversion may be formulated in a latent space where a lowdimensional parameterization of the patterns is defined and where Markov chain Monte Carlo or gradient-based methods may be applied. However, the generative mapping between the latent and the original (pixel) representations is usually highly nonlinear which may cause some difficulties for inversion, especially for gradientbased methods. In this contribution we review the conceptual framework of inversion with DGMs and propose that this nonlinearity is caused mainly by changes in topology and curvature induced by the generative function. As a result, we identify a conflict between two goals: the accuracy of the generated patterns and the feasibility of gradient-based inversion. In addition, we show how some of the training parameters of a variational autoencoder, which is a particular instance of a DGM, may be chosen so that a tradeoff between these two goals is achieved and acceptable inversion results are obtained with a stochastic gradient-descent scheme. A series of test cases using synthetic models with channel patterns of different complexity and cross-borehole traveltime tomographic data involving both a linear and a nonlinear forward operator show that the proposed method provides useful results and performs better compared to previous approaches using DGMs with gradient-based inversion.

关键词： Deep generative model Geophysical inversion Geological prior information variational autoencoder Stochastic gradient descent Deep learning

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Reconstructing a quantum state with a variational autoencoder

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INTERNATIONAL JOURNAL OF QUANTUM INFORMATION 2021年第8期19卷 2140005-2140005页

作者： Chen, Chuangtao He, Zhimin Huang, Zhiming Situ, Haozhen Foshan Univ Sch Elect & Informat Engn 33 Guangyun Rd Foshan 528225 Guangdong Peoples R China Wuyi Univ Sch Econ & Management 22 Dongcheng Village Jiangmen 529020 Guangdong Peoples R China South China Agr Univ Coll Math & Informat 483 Wushan Rd Guangzhou 510642 Guangdong Peoples R China

Quantum state tomography (QST) is an important and challenging task in the field of quantum information, which has attracted a lot of attentions in recent years. Machine learning models can provide a classical representation of the quantum state after trained on the measurement outcomes, which are part of effective techniques to solve QST problem. In this work, we use a variational autoencoder (VAE) to learn the measurement distribution of two quantum states generated by MPS circuits. We first consider the Greenberger-Horne-Zeilinger (GHZ) state which can be generated by a simple MPS circuit. Simulation results show that a VAE can reconstruct 3- to 8-qubit GHZ states with a high fidelity, i.e., 0.99, and is robust to depolarizing noise. The minimum number (N-s(*)) of training samples required to reconstruct the GHZ state up to 0.99 fidelity scales approximately linearly with the number of qubits (N). However, for the quantum state generated by a complex MPS circuit, N-s(*) increases exponentially with N, especially for the quantum state with high entanglement entropy.

关键词： Quantum state tomography quantum machine learning variational autoencoder

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A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network

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NUCLEAR FUSION 2021年第12期61卷 126063-126063页

作者： Wei, Y. Levesque, J. P. Hansen, C. J. Mauel, M. E. Navratil, G. A. Columbia Univ Dept Appl Phys & Appl Math New York NY 10027 USA Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA

A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the operational stability boundary of tokamak plasma discharges. This model was implemented using a dataset of over 3000 discharges from the high beta tokamak-extended pulse (HBT-EP) device. We found the VAE model to be capable of forming a continuous low-dimensional operational space map and identifying the operational boundaries using a specified warning time window. By projecting the operational parameters onto the same reduced space, this provides an intuitive way for the machine operator or an automated control system to perform disruption avoidance using a relevant control actuator as a discharge approaches a boundary. Pre-programmed GPU control experiments were conducted to demonstrate this control technique using HBT-EP's saddle control coils as a horizontal position actuator, showing the ability to avoid the oncoming disruptive event and extend the duration of the discharge.

关键词： disruption avoidance machine learning neural network variational autoencoder HBT-EP feedback control

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A SEMI-SUPERVISED APPROACH FOR IDENTIFYING ABNORMAL HEART SOUNDS USING variational autoencoder

A SEMI-SUPERVISED APPROACH FOR IDENTIFYING ABNORMAL HEART SO...

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

作者： Banerjee, Rohan Ghose, Avik Tata Consultancy Serv Res & Innovat Mumbai Maharashtra India

ISBN: (纸本)9781509066315

Abnormal heart sounds may have diverse frequency characteristics depending upon underlying pathological conditions. Designing a binary classifier for predicting normal and abnormal heart sounds using supervised learning requires a lot of training data, covering different types of cardiac abnormalities. In this paper, we propose a semi-supervised approach to solve the problem. A convolutional variational autoencoder (VAE) structure is defined for learning the probability distribution of the spectrogram properties of normal heart sounds. The Kullback-Leibler (KL) divergence between the known prior distribution of the VAE and the encoded distribution is taken as an anomaly score for detecting abnormal heart sounds. The proposed approach is evaluated on open access and in-house datasets of Phonocardiogram (PCG) signals, recorded from normal subjects and patients, having cardiovascular diseases, cardiac murmurs and extra heart sounds. Results show that an improved classification performance is achieved in comparison to the existing approaches.

关键词： Heart sounds variational autoencoder Semi-supervised learning Convolutional Neural Network

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