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variational autoencoder or Generative Adversarial Networks? A Comparison of Two Deep Learning Methods for Flow and Transport Data Assimilation

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MATHEMATICAL GEOSCIENCES 2022年第6期54卷 1017-1042页

作者： Bao, Jichao Li, Liangping Davis, Arden South Dakota Sch Mines & Technol Dept Geol & Geol Engn Rapid City SD 57701 USA

Groundwater modeling is an important tool for water resources management and aquifer remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of observed data pose major challenges for groundwater flow and contaminant transport modeling. Data assimilation such as the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) can be used to improve the understanding of the subsurface by integrating a variety of data into the modeling. For highly heterogeneous aquifers such as fluvial deposits, traditional data assimilation methods cannot preserve geological structures. In this work, two of the most popular deep learning methods, variational autoencoder (VAE) and Generative Adversarial Network (GAN), are compared for identifying geological structures using flow and transport data assimilation. Specifically, VAE and GAN are used to re-parameterize the hydraulic conductivity fields with low dimensional latent variables. The ES-MDA then is used to update the latent variables by assimilating the observed data such as hydraulic head and contaminant concentration into the model. Synthetic examples of both categorical and continuous variables are conducted to test the performance of coupling ES-MDA with VAE or GAN. The results demonstrate that the generating quality of GAN is better such that channels are generated with similar properties as in the training image, while VAE has the advantage that data assimilation is more successful in allowing better localization of the actual channels for the considered inverse problem.

关键词： Deep generative models variational autoencoder Generative adversarial network Ensemble smoother Data assimilation Heterogeneity

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variational autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images

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NEW GENERATION COMPUTING 2023年第1期41卷 25-60页

作者： Chatterjee, Sankhadeep Maity, Soumyajit Bhattacharjee, Mayukh Banerjee, Soumen Das, Asit Kumar Ding, Weiping Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur W Bengal India Univ Engn & Management Dept Comp Sci & Engn Kolkata W Bengal India Budge Budge Inst Technol Dept Elect & Commun Engn Kolkata 700137 W Bengal India Nantong Univ Sch Informat Sci & Technol Nantong 226019 Jiangsu Peoples R China

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., "COVID-19", "Pneumonia", and "Normal". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.

关键词： COVID-19 Class imbalance variational autoencoder Oversampling Undersampling

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variational autoencoder based on distributional semantic embedding and cross-modal reconstruction for generalized zero-shot fault diagnosis of industrial processes

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 177卷 1154-1167页

作者： Mou, Miao Zhao, Xiaoqiang Liu, Kai Hui, Yongyong Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou Peoples R China Lanzhou Univ Technol Gansu Key Lab Adv Control Ind Proc Lanzhou Peoples R China

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. To solve this problem, a generalized zero-shot fault diagnosis model for industrial processes based on distributional semantic embedding and cross-modal reconstruction variational autoencoder (DSECMR-VAE) is proposed. DSECMR-VAE uses two variational autocoders (VAEs) to encode the features from two different modalities of fault samples and fault attribute semantic vectors into latent variables, and then uses the latent variables to generate fault samples belonging to the seen and unseen fault classes. In addition, a Barlow matrix is designed specifically for the distribution parameters of the latent variables, this matrix is utilized to measure the consistency between the distribution of fault samples and fault attribute semantic vectors. Subsequently, cross-modal reconstruction is performed on VAE. Cross-modal reconstruction uses the input from different modalities to reconstruct the current input, which can fully combine the information from different modalities. Finally, a classifier is trained based on the obtained latent variables to realize the generalized zero-shot fault diagnosis. Two case studies demonstrate the effectiveness and superiority of the proposed model for zero-shot and generalized zero-shot fault diagnosis.

关键词： Fault diagnosis Generalized zero-shot learning variational autoencoder Attribute semantic description Distributional embedding Cross reconstruction

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variational autoencoder-Based Multiobjective Topology Optimization of Electrical Machines Using Vector Graphics

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IEEE ACCESS 2024年 12卷 184813-184826页

作者： Heroth, Michael Schmid, Helmut C. Gregorova, Magda Herrler, Rainer Hofmann, Wilfried ZF Friedrichshafen AG Adv Dev E Motor Simulat D-88046 Schweinfurt Germany TH Wurzburg Schweinfurt CAIRO D-97421 Wurzburg Germany Tech Univ Dresden Chair Elect Machines & Drives D-01069 Dresden Germany

The increasing automation in the design process of electrical machines for vehicles generates huge amounts of data, leading to a growing interest in using machine learning for faster predictions and optimization. This paper presents an innovative approach to simultaneously model and optimize different geometries of electrical machines. Unlike previous methods that rely on specific design variables or pixel-based representations, this approach is based on a flexible vector graphic representation that can easily be applied to any electrical machine configuration. This also sidesteps the problem of scalar-based design representation and, unlike pixel-based representation, can be directly imported into finite element simulations for validation. To speed up the optimization process, we transform the corresponding problem into a lower-dimensional latent space learned by a variational autoencoder. This is trained on a total of 6839 different 2D geometries of electrical machines, which were exported from the finite element simulation into the standardized scalable vector graphics format. The optimization results show that a sharp boundary is formed in the combined Pareto front of different rotor topologies. The validation results demonstrate that this novel method delivers results comparable to those of current techniques.

关键词： Geometry Optimization Topology Graphics Vectors Training data Rotors Training Databases Synchronous machines Deep neural network multiobjective optimization permanent magnet synchronous machine scalable vector graphics variational autoencoder

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variational autoencoder-Based Dimensionality Reduction for High-Dimensional Small-Sample Data Classification

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2020年第1期19卷

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

Classification problems in which the number of features (dimensions) is unduly higher than the number of samples (observations) is an essential research and application area in a variety of domains, especially in computational biology. It is also known as a high-dimensional small-sample-size (HDSSS) problem. Various dimensionality reduction methods have been developed, but they are not potent with the small-sample-sized high-dimensional datasets and suffer from overfitting and high-variance gradients. To overcome the pitfalls of sample size and dimensionality, this study employed variational autoencoder (VAE), which is a dynamic framework for unsupervised learning in recent years. The objective of this study is to investigate a reliable classification model for high-dimensional and small-sample-sized datasets with minimal error. Moreover, it evaluated the strength of different architectures of VAE on the HDSSS datasets. In the experiment, six genomic microarray datasets from Kent Ridge Biomedical Dataset Repository were selected, and several choices of dimensions (features) were applied for data preprocessing. Also, to evaluate the classification accuracy and to find a stable and suitable classifier, nine state-of-the-art classifiers that have been successful for classification tasks in high-dimensional data settings were selected. The experimental results demonstrate that the VAE can provide superior performance compared to traditional methods such as PCA, fastlCA, FA, NMF, and LDA in terms of accuracy and AUROC.

关键词： High-dimensional and small-sample size dataset variational autoencoder classification computational biology deep learning

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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-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives

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Autonomous Intelligent Systems 2024年第1期4卷 297-306页

作者： Marius Benkert Michael Heroth Rainer Herrler Magda Gregorová Helmut C.Schmid CAIRO THWSFranz-Horn-Str 2Würzburg97082Germany Advanced Development E-Motor Simulation ZF Friedrichshafen AGRotgenstr.297424 SchweinfurtGermany

The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element *** methodologies are not only resource-intensive,but also *** learning models can be used to shortcut these ***,challenges arise when considering the unique parameter sets specific to each machine *** on two recent studies(Parekh et *** IEEE ***.58(9):1-4,2022;Parekh et al.,Deep learning based meta-modeling for multi-objective technology optimization of electrical machines,2023,arXiv:2306.09087),that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization,this paper proposes a refined architecture and optimization *** modifications aim to streamline and enhance the robustness of both the training and optimization processes,and compare the results with the variational autoencoder architecture proposed recently.

关键词： Deep learning Design optimization Electrical machines variational autoencoder

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variational autoencoder-Based Deep Neural Network for Coronary Heart Disease Risk Prediction 1

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17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) / 14th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Amarbayasgalan, Tsatsral Park, Kwang Ho Davagdorj, Khishigsuren Ryu, Keun Ho Theera-Umpon, Nipon Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chungbuk Natl Univ Dept Comp Sci Coll Elect & Comp Engn Cheongju 28644 South Korea Chiang Mai Univ Inst Biomed Engn Chiang Mai 50200 Thailand

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

Coronary heart disease (CHD) is one of the top causes of global mortality. Most patients cannot be diagnosed at the early stage because it does not give any symptoms for many years. If CHD gets worse, it will require advanced treatments, such as heart transplant and stent surgery. Therefore, it is useful in preventing CHD by predicting high-risk people who will suffer from CHD. In this study, we have proposed a variational autoencoder (VAE)-based deep neural network (DNN) model for predicting CHD risk. We improved the performance of DNN model by enriching training dataset using VAE-based data generation. The proposed method has been compared with machine learning algorithms on Korea National Health and Nutrition Examination Survey (KNHANES) dataset. As a result, the proposed method outperformed the compared classifiers. The performance measurements include accuracy, precision, recall, F1-score, and AUC score which reached 0.851, 0.882, 0.809%, 0.844, and 0.851, respectively.

关键词： Coronary heart disease Deep neural network Machine learning variational autoencoder

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variational autoencoder BASED UNSUPERVISED DOMAIN ADAPTATION FOR SEMANTIC SEGMENTATION

VARIATIONAL AUTOENCODER BASED UNSUPERVISED DOMAIN ADAPTATION...

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IEEE International Conference on Image Processing (ICIP)

作者： Li, Zongyao Togo, Ren Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Educ & Res Ctr Math & Data Sci Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781728163956

Unsupervised domain adaptation, which transfers supervised knowledge from a labeled domain to an unlabeled domain, remains a tough problem in the field of computer vision, especially for semantic segmentation. Some methods inspired by adversarial learning and semi-supervised learning have been developed for unsupervised domain adaptation in semantic segmentation and achieved outstanding performances. In this paper, we propose a novel method for this task. Like adversarial learning-based methods using a discriminator to align the feature distributions from different domains, we employ a variational autoencoder to get to the same destination but in a non-adversarial manner. Since the two approaches are compatible, we also integrate an adversarial loss into our method. By further introducing pseudo labels, our method can achieve state-of-the-art performances on two benchmark adaptation scenarios, GTA5-to-CITYSCAPES and SYNTHIA-to-CITYSCAPES.

关键词： Unsupervised domain adaptation semantic segmentation variational autoencoder adversarial learning

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variational autoencoder FOR SPEECH ENHANCEMENT WITH A NOISE-AWARE ENCODER

VARIATIONAL AUTOENCODER FOR SPEECH ENHANCEMENT WITH A NOISE-...

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

作者： Fang, Huajian Carbajal, Guillaume Wermter, Stefan Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany Univ Hamburg Knowledge Technol WTM Hamburg Germany

ISBN: (纸本)9781728176055

Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making the estimation particularly sensitive to noise presence, especially in low signal-to-noise ratios (SNRs). To increase the robustness of the VAE, we propose to include noise information in the training phase by using a noise-aware encoder trained on noisy-clean speech pairs. We evaluate our approach on real recordings of different noisy environments and acoustic conditions using two different noise datasets. We show that our proposed noise-aware VAE outperforms the standard VAE in terms of overall distortion without increasing the number of model parameters. At the same time, we demonstrate that our model is capable of generalizing to unseen noise conditions better than a supervised feedforward deep neural network (DNN). Furthermore, we demonstrate the robustness of the model performance to a reduction of the noisy-clean speech training data size.

关键词： speech enhancement generative model variational autoencoder semi-supervised learning

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