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IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

作者： Dahale, Shweta Munikoti, Sai Natarajan, Balasubramaniam Yang, Rui Kansas State Univ Elect & Comp Engn Manhattan KS 66506 USA Natl Renewable Energy Lab NREL Golden CO USA Natl Renewable Energy Lab NREL Sensing & Predict Analyt Grp Power Syst Engn Ctr Golden CO 80401 USA Kansas State Univ Elect & Comp Engn Manhattan KS 66506 USA

ISBN: (纸本)9781665453554

Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measurements may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.

关键词： Smart distribution system Multi time-scale measurements Neural ordinary differential equations variational autoencoder

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WEAKLY SUPERVISED DISENTANGLEMENT WITH TRIPLET NETWORK 30

WEAKLY SUPERVISED DISENTANGLEMENT WITH TRIPLET NETWORK

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30th IEEE International Conference on Image Processing (ICIP)

作者： Coutinho, Pedro C. C. C. C. Berthoumieu, Yannick Donias, Marc Guillon, Sebastien TotalEnergies OneTech Paris France Univ Bordeaux CNRS Bordeaux INP IMSUMR 5218 Bordeaux France

ISBN: (纸本)9781728198354

variational autoencoders have gained considerable attention due to their capacity of encoding high dimensional data into a lower dimensional latent space. In this context, several methods have been proposed with the objective of producing disentangled representations. In this work, we propose a weakly supervised model that explicitly disentangles the factors of variation of a dataset in separate subspaces using a pairwise architecture. We also create a framework that encourages conditional image generation according to the desired factor of variation, by controlling these subspaces. This is achieved by introducing an additional network trained with a triplet loss. Its output approximates representations of images generated from the same factor and push the ones of images generated from different factors apart. Experiments are carried out on widely used datasets, and show that our model is able to disentangle specified factors of variation, and to generate new data while constraining desired properties, even when these factors have small influence on reconstruction loss.

关键词： Disentanglement Factors of Variation Generative Model variational autoencoder Triplet Network

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The road from MLE to EM to VAE: A brief tutorial

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AI OPEN 2022年 3卷 29-34页

作者： Ding, Ming Tsinghua Univ Beijing Peoples R China

variational Auto -Encoders (VAEs) have emerged as one of the most popular genres of generative models , which are learned to characterize the data distribution. The classic Expectation Maximization (EM) algorithm aims to learn models with hidden variables. Essentially, both of them are iteratively optimizing the evidence lower bound (ELBO) to maximize to the likelihood of the observed data. This short tutorial joins them up into a line and offer a good way to thoroughly understand EM and VAE with minimal knowledge. It is especially helpful to beginners and readers with experiences in machine learning applications but no statistics background.

关键词： Generative model variational autoencoder

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Semi-supervised Cavitation Detection for Centrifugal Pumps

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TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A 2022年第2期46卷 153-162页

作者： Yoo, Donghwi Choi, Minseok Kim, Chungeon Oh, Hyunseok Gwangju Inst Sci & Technol Sch Mech Engn Gwangju South Korea

Cavitation is a dominant failure mode that accelerates the wear and deterioration of pumps. Cavitation can lead to pump malfunction and, eventually, catastrophic failure of the whole system. Therefore, it is important to avoid cavitation in the pump. This paper proposes a semi-supervised learning method that detects cavitation in centrifugal pumps. One-dimensional (1D) vibration signals are converted into two-dimensional (2D) images by the short time Fourier transform. The severity of the cavitation is determined using the variational autoencoder and Mahalanobis distance. The effectiveness of the proposed method is evaluated using the data collected from a 0.75 kW hydraulic pump testbed. It is confirmed that the proposed method can detect cavitation with different severities and help avoid the cavitation phenomenon.

关键词： Anomaly Detection Short Time Fourier Transform variational autoencoder Mahalanobis Distance

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variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第12期41卷 3552-3561页

作者： Zou, Qing Ahmed, Abdul Haseeb Nagpal, Prashant Priya, Sarv Schulte, Rolf F. Jacob, Mathews Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Philips Healthcare Rochester MN 55311 USA Univ Wisconsin Madison Dept Radiol Madison WI 53706 USA Univ Iowa Dept Radiol Iowa City IA 52242 USA Gen Elect Healthcare D-80807 Munich Germany

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multi-slice free breathing and ungated cardiac MRI data from highly undersampled measurements. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions.

关键词： variational autoencoder generative model CNN manifold approach unsupervised learning free breathing cardiac magnetic resonance imaging (MRI) image reconstruction

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Emotional Voice Conversion with Semi-Supervised Generative Modeling 24

Emotional Voice Conversion with Semi-Supervised Generative M...

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Interspeech Conference

作者： Zhu, Hai Zhan, Huayi Cheng, Hong Wu, Ying Sichuan Changhong Elect Holding Grp Co Ltd Changhong AI Lab CHAIR Mianyang Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Robot Chengdu Sichuan Peoples R China Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

Emotional Voice Conversion (EVC) is a task that aims to convert the emotional state of speech from one to another while preserving the linguistic information and identity of the speaker. However, many studies are limited by the requirement for parallel speech data between different emotional patterns, which is not widely available in real-life applications. Furthermore, the annotation of emotional data is highly time-consuming and labor-intensive. To address these problems, in this paper, we propose SGEVC, a novel semi-supervised generative model for emotional voice conversion. This paper demonstrates that using as little as 1% supervised data is sufficient to achieve EVC. Experimental results show that our proposed model achieves state-of-the-art (SOTA) performance and consistently outperforms EVC baseline frameworks.

关键词： emotional voice conversion variational autoencoder semi-supervised end-to-end

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Supervised Electrocardiogram(ECG) Features Outperform Knowledge-based And Unsupervised Features In Individualized Survival Prediction 3

Supervised Electrocardiogram(ECG) Features Outperform Knowle...

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3rd Machine Learning for Health Symposium

作者： Nademi, Yousef Kalmady, Sunil V. Sun, Weijie Qi, Shi-ang Hindle, Abram Kaul, Padma Greiner, Russell Univ Alberta Dept Comp Sci Edmonton AB Canada Univ Alberta Dept Med Canadian VIGOUR Ctr Edmonton AB Canada Univ Alberta Dept Med Edmonton AB Canada Alberta Machine Intelligence Inst Edmonton AB Canada

An electrocardiogram (ECG) provides crucial information about an individual's health status. Researchers utilize ECG data to develop learners for a variety of tasks, ranging from diagnosing ECG abnormalities to estimating time to death - here modeled as individual survival distributions (ISDs). The way the ECG is represented is important for creating an effective learner. While many traditional ECG-based prediction models rely on hand-crafted features, such as heart rate, this study aims to achieve a better representation. The effectiveness of various ECG based feature extraction methods for prediction of ISDs, either supervised or unsupervised, have not been explored previously. The study uses a large ECG dataset from 244,077 patients with over 1.6 million 12lead ECGs, each labeled with the patient's disease - one or more International Classification of Diseases (ICD) codes. We explored extracting high-level features from ECG traces using various approaches, then trained models that used these ECG features (along with age and sex), across a range of training sizes, to estimate patient-specific ISDs. The results showed that the supervised feature extractor method produced ECG features that can estimate ISD curves better than ECG features obtained from unsupervised or knowledge-based methods. Supervised ECG features required fewer training instances (as low as 500) to learn ISD models that performed better than the baseline model that only used age and sex. On the other hand, unsupervised and knowledge-based ECG features required over 5,000 training samples to produce ISD models that performed better than the baseline. The study's findings may assist researchers in selecting the most appropriate approach for extracting high-level features from ECG signals to estimate patient-specific ISD curves.

关键词： Electrocardiogram Individual Survival Distributions variational autoencoder

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TransVQ-VAE: Generating Diverse Images Using Hierarchical Representation Learning 1

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Jin, Chuan Zheng, Anqi Wu, Zhaoying Tong, Changqing Hangzhou Dianzi Univ Sch Sci Hangzhou 310018 Peoples R China Southeast Univ Southeast Monash Joint Grad Sch Suzhou 210096 Peoples R China

ISBN: (数字)9783031442131

ISBN: (纸本)9783031442124;9783031442131

Understanding how to learn feature representations for images and generate high-quality images under unsupervised learning was challenging. One of the main difficulties in feature learning has been the problem of posterior collapse in variational inference. This paper proposes a hierarchical aggregated vector-quantized variational autoencoder, called TransVQ-VAE. Firstly, the multi-scale feature information based on the hierarchical Transformer is complementarily encoded to represent the global and structural dependencies of the input features. Then, it is compared to the latent encoding space with a linear difference to reduce the feature dimensionality. Finally, the decoder generates synthetic samples with higher diversity and fidelity compared to previous ones. In addition, we propose a dual self-attention module in the encoding process that uses spatial and channel information to capture distant texture correlations, contributing to the consistency and realism of the generated images. Experimental results on MNIST, CIFAR-10, CelebA-HQ, and ImageNet datasets show that our approach significantly improves the diversity and visual quality of the generated images.

关键词： Image generation Unsupervised learning variational autoencoder Hierarchical representation Dual self-attention

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Automatic Generation of Semantic Parts for Face Image Synthesis 1

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22nd International Conference on Image Analysis and Processing (ICIAP)

作者： Fontanini, Tomaso Ferrari, Claudio Bertozzi, Massimo Prati, Andrea Univ Parma Dept Engn & Architecture IMP Lab Parma Italy

ISBN: (数字)9783031431487

ISBN: (纸本)9783031431470;9783031431487

Semantic image synthesis (SIS) refers to the problem of generating realistic imagery given a semantic segmentation mask that defines the spatial layout of object classes. Most of the approaches in the literature, other than the quality of the generated images, put effort in finding solutions to increase the generation diversity in terms of style i.e. texture. However, they all neglect a different feature, which is the possibility of manipulating the layout provided by the mask. Currently, the only way to do so is manually by means of graphical users interfaces. In this paper, we describe a network architecture to address the problem of automatically manipulating or generating the shape of object classes in semantic segmentation masks, with specific focus on human faces. Our proposed model allows embedding the mask class-wise into a latent space where each class embedding can be independently edited. Then, a bi-directional LSTM block and a convolutional decoder output a new, locally manipulated mask. We report quantitative and qualitative results on the CelebMask-HQ dataset, which show our model can both faithfully reconstruct and modify a segmentation mask at the class level. Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture. Code available at https://***/TFonta/Semantic-VAE.

关键词： Image Synthesis variational autoencoder Face Editing

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TimToShape: Supporting Practice of Musical Instruments by Visualizing Timbre with 2D Shapes based on Crossmodal Correspondences 23

TimToShape: Supporting Practice of Musical Instruments by Vi...

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28th Annual Conference on Intelligent User Interfaces (IUI)

作者： Arai, Kota Hirao, Yutaro Narumi, Takuji Nakamura, Tomohiko Takamichi, Shinnosuke Yoshida, Shigeo Univ Tokyo Tokyo Japan OMRON SINIC X Corp Tokyo Japan

ISBN: (纸本)9798400701061

Timbre is high-dimensional and sensuous, making it difficult for musical-instrument learners to improve their timbre. Although some systems exist to improve timbre, they require expert labeling for timbre evaluation;however, solely visualizing the results of unsupervised learning lacks the intuitiveness of feedback because human perception is not considered. Therefore, we employ crossmodal correspondences for intuitive visualization of the timbre. We designed TimToShape, a system that visualizes timbre with 2D shapes based on the user's input of timbre-shape correspondences. TimToShape generates a shape morphed by linear interpolation according to the timbre's position in the latent space, which is obtained by unsupervised learning with a variational autoencoder (VAE). We confirmed that people perceived shapes generated by TimToShape to correspond more to timbre than randomly generated shapes. Furthermore, a user study of six violin players revealed that TimToShape was well-received in terms of visual clarity and interpretability.

关键词： crossmodal correspondences timbre-shape correspondences timbre musical instrumental practice variational autoencoder

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