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Proceedings of the 10th International Conference on Climate Informatics

作者： Saumya Sinha Sophie Giffard-Roisin Fatima Karbou Michael Deschatres Anna Karas Nicolas Eckert Cécile Coléou Claire Monteleoni University of Colorado Boulder USA ISTerre Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS IRD IFSTTAR France CNRM-GAME Météo-France Grenoble France Irstea Université Grenoble Alpes France Irstea Université Grenoble Alpes France

ISBN: (纸本)9781450388481

This work demonstrates that deep unsupervised learning holds much promise for rare event detection, even when labeled data is limited. Rare event detection is challenging for traditional supervised learning approaches due to high class-imbalance. We demonstrate the efficacy of deep unsupervised learning, in particular a variational autoencoder (VAE), when used for anomaly detection, in an application to avalanche detection in the French Alps, from satellite SAR imagery and a limited on-the-ground survey. Remarkably, our results demonstrate that supervision (i.e., access to labeled data) is only needed in the validation phase of our training pipeline, to tune a hyper-parameter: the threshold on reconstruction error of the VAE (trained in an unsupervised fashion) that will be used to designate an observation as an anomaly, i.e., an avalanche in this context. Our method outperforms previous benchmarks in avalanche detection, including supervised learning a convolutional neural network on an artificially balanced version of the same data. To the best of our knowledge, this is the first step in exploring the potential of applying deep unsupervised learning methods to detect avalanche deposits. Our proposed semi-supervised learning pipeline is also promising for real-world settings in which the data is very unbalanced and labels are limited.

关键词： unsupervised deep learning variational autoencoder anomaly detection avalanche detection satellite imaging

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Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第11-12期111卷 3597-3611页

作者： Proteau, Antoine Zemouri, Ryad Tahan, Antoine Thomas, Marc Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada HESAM Univ CEDRIC Lab Conservatoire Natl Arts & Metiers CNAM F-750141 Paris France

In this paper, we applied a variational autoencoder approach to an industrial machining problematic. We proposed a model based on a two-steps training process and a two-dimensional latent space. This two-dimensional latent space has better dimension reduction capability compared to a principal component analysis, which would require 24 components to express 90.0% of the variation. Moreover, the proposed model is shown capable of classifying a cutting operation based solely on data obtained from sensors mounted on a CNC machine, with an accuracy of 99.24%. The suggested model is also shown capable to be an efficient visual process monitoring tool capable of detecting early changes of state in a machining process. We show that this approach can visually identify defect caused by an increase of less than 1% of the energy in the signal, which is earlier than conventional monitoring methods. Additionally, our work is based on an industrial dataset acquired during regular production. This increases the opportunity for technological transfer when it comes to better understanding and better monitoring early changes in a machining process.

关键词： variational autoencoder Dimension reduction 2D-visualization Process monitoring Early detection Machining process

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Condition-Transforming variational autoencoder for Generating Diverse Short Text Conversations

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2020年第6期19卷 1–13页

作者： Ruan, Yu-Ping Ling, Zhen-Hua Zhu, Xiaodan Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc 443 Huangshan Rd Hefei Peoples R China Queens Univ Dept Elect & Comp Engn Walter Light HallUnion St Kingston ON K7L 2N1 Canada

In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVARs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N(0.1). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover's distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses.

关键词： Neural network variational autoencoder conversation text generation

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GBNF-VAE: A Pathological Voice Enhancement Model Based on Gold Section for Bottleneck Feature With variational autoencoder

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Journal of Voice 2023年

作者： Liu, Ganjun Zhang, Tao Ding, Biyun Lv, Ying Hou, Xiaohui Guo, Haoyang Wu, Yaqin Fu, Dehui School of Electrical and Information Engineering Tianjin University Tianjin China School of Software Shanxi Agricultural University Shanxi China Department of Otolaryngology Tianjin Medical University Tianjin China

Objective: Speech enhancement has become a promising technique to accommodate demands of the improvement in quality of a degraded speech signal. The main works now focus on separating normal speech from noise, but have neglected the low quality of impaired speech influenced by anomalous glottis flow. In order to effectively enhance the pathological speech, it is essential to design a separation mechanism for extracting high-dimensional timbre features and speech features separately to suppress low-dimensional noises. Methods: In this paper, we propose an enhancement model GBNF-VAE to extract timbre efficiently by reducing anomalous airflow noise interference, and by combining the semantic features with timbre features to synthesize the enhanced speech. In particular, the bottleneck feature can characterize the timbre by the controlled number of nodes through the Golden Section method, which effectively improves computational efficiency. In addition, variational autoencoder is adopted to extract semantic features which are combined with the previous timbre features to synthesize the enhanced speech. Results: Finally, spectrum observation, objective indicators and subjective evaluation all show the outstanding performance of GBNF-VAE in pathological speech quality enhancement. © 2023 The Voice Foundation

关键词： Bottleneck feature Golden section Pathological speech enhancement variational autoencoder

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variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex

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NEUROIMAGE 2019年 198卷 125-136页

作者： Han, Kuan Wen, Haiguang Shi, Junxing Lu, Kun-Han Zhang, Yizhen Fu, Di Liu, Zhongming Weldon Sch Biomed Engn W Lafayette IN USA Sch Elect & Comp Engn W Lafayette IN USA Purdue Univ Purdue Inst Integrat Neurosci W Lafayette IN 47906 USA

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Using the trained VAE, we predicted and decoded cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE could predict the video-evoked cortical responses with comparable accuracy in early visual areas, but relatively lower accuracy in higher-order visual areas. The distinction between CNN and VAE in terms of encoding performance was primarily attributed to their different learning objectives, rather than their different model architecture or number of parameters. Despite lower encoding accuracies, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least squares regression, for being able to reconstruct both the spatial structure and color of the visual input. Such findings highlight VAE as an unsupervised model for learning visual representation, as well as its potential and limitations for explaining cortical responses and reconstructing naturalistic and diverse visual experiences.

关键词： Neural coding variational autoencoder Bayesian brain Visual reconstruction

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variational-autoencoder Siamese Network for Change Detection on Satellite Imagery

Variational-AutoEncoder Siamese Network for Change Detection...

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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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