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检索条件"主题词=Variational autoencoder"

共 1537 条记录，以下是1291-1300 订阅

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Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches 9

Generating Synthetic Time Series for Machine-Learning-Empowe...

引用

9th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Westmeier, Tobias Botache, Diego Bieshaar, Maarten Sick, Bernhard Univ Kassel Dept Elect Engn & Comp Sci Kassel Germany Bosch Ctr Artificial Intelligence Renningen Germany

ISBN: (纸本)9781665473309

The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reduce development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances. Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Fr ' echet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.

关键词： Monitoring Failure Detection Electric Motor Test Benches Deep Neuronal Networks Synthetic Data Generation Time Series autoencoder variational autoencoder

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Enhanced variational U-Net for Weather Forecasting

Enhanced Variational U-Net for Weather Forecasting

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Kwok, Pak Hay Qi, Qi

ISBN: (纸本)9781665439022

This work describes our third-place solution in both the core and transfer learning challenges of Weather4cast - IEEE BigData Cup. The solution builds on our success in Weather4cast - Stage 1 [1], and uses the same variational U-Net architecture. Building on the lessons learned from Weather4cast - Stage 1, we enhanced the model's performance through the use of data augmentations and model blending. Furthermore, we explored transfer learning between the two competitions. The code for this solution is available at https://***/qiq208/w4c-2021IEEE

关键词： weather forecasting machine learning convolutional neural networks variational autoencoder

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UNSUPERVISED REPRESENTATION LEARNING OF SPEECH FOR DIALECT IDENTIFICATION

UNSUPERVISED REPRESENTATION LEARNING OF SPEECH FOR DIALECT I...

引用

IEEE Workshop on Spoken Language Technology (SLT)

作者： Shon, Suwon Hsu, Wei-Ning Glass, James MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781538643341

In this paper, we explore the use of a factorized hierarchical variational autoencoder (FHVAE) model to learn an unsupervised latent representation for dialect identification (DID). An FHVAE can learn a latent space that separates the more static attributes within an utterance from the more dynamic attributes by encoding them into two different sets of latent variables. Useful factors for dialect identification, such as phonetic or linguistic content, are encoded by a segmental latent variable, while irrelevant factors that are relatively constant within a sequence, such as a channel or a speaker information, are encoded by a sequential latent variable. The disentanglement property makes the segmental latent variable less susceptible to channel and speaker variation, and thus reduces degradation from channel domain mismatch. We demonstrate that on fully-supervised DID tasks, an end-to-end model trained on the features extracted from the FHVAE model achieves the best performance, compared to the same model trained on conventional acoustic features and an i-vector based system. Moreover, we also show that the proposed approach can leverage a large amount of unlabeled data for FHVAE training to learn domain-invariant features for DID, and significantly improve the performance in a low-resource condition, where the labels for the in-domain data are not available.

关键词： language recognition dialect identification variational autoencoder unsupervised learning

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variational Intensity Cross Channel Encoder for Unsupervised Vessel Segmentation on OCT Angiography

Variational Intensity Cross Channel Encoder for Unsupervised...

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Medical Imaging Conference - Image Processing

作者： Liu, Yihao Zuo, Lianrui Carass, Aaron He, Yufan Filippatou, Angeliki Solomon, Sharon D. Saidha, Shiv Calabresi, Peter A. Prince, Jerry L. Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Sch Med Dept Neurol Baltimore MD 21205 USA Johns Hopkins Univ Sch Med Wilmer Eye Inst Baltimore MD 21205 USA

ISBN: (纸本)9781510633940

Deep learning approaches have been used extensively for medical image segmentation tasks. Training deep networks for segmentation, however, typically requires manually delineated examples which provide a ground truth for optimization of the network. In this work, we present a neural network architecture that segments vascular structures in retinal OCTA images without the need of direct supervision. Instead, we propose a variational intensity cross channel encoder that finds vessel masks by exploiting the common underlying structure shared by two OCTA images of the the same region but acquired on different devices. Experimental results demonstrate significant improvement over three existing methods that are commonly used.

关键词： OCTA Vessel segmentation variational autoencoder Unsupervised segmentation Image synthesis

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Modeling Global Latent Semantic in Multi-Turn Conversations with Random Context Reconstruction 48

Modeling Global Latent Semantic in Multi-Turn Conversations ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Chengwen Wu, Danqin Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts & Telecommunications Beijing100876 China

ISBN: (纸本)9781728163277

In multi-turn dialogue generation, it's still a challenge to generate coherent responses given dialogue history, which requires neural models to learn complex semantic structures between responses and contexts. It's practical that a multi-turn conversation takes place under a constant background, such as dialog scene, style and topic, which have an important effect on the distribution of words and phrases in conversations. However, such global semantics haven't been exploited in recent Transformer-based dialog models. In this paper, we propose a Global semantic-guided variational Dialog (GVDialog) model, which introduces a variational autoencoder (VAE) into basic Transformer-based hierarchical dialogue models and use Random Context Reconstruction (RCR) task to compress global semantics into latent space without any timeconsuming human annotation. The latent variables, interpreted as holistic attributes of dialogue history, could guide the response decoder to generate coherent utterances from a global level. Experiments are conducted on Chinese data Douban and English data Cornell Movie. Evaluation results show the effectiveness and superiority of GVDialog compared to other hierarchical dialog models. © 2023 IEEE.

关键词： Dialog generation Hierarchical network Latent Space variational autoencoder

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Generative Modeling Based Manifold Learning for Adaptive Filtering Guidance 48

Generative Modeling Based Manifold Learning for Adaptive Fil...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Helwani, Karim Smaragdis, Paris Goodwin, Michael M. Amazon Web Services United States University of Illinois Urbana-Champaign United States

ISBN: (纸本)9781728163277

In most practical adaptive filtering problems, estimated filters are not arbitrary, but instead lie on a manifold that encapsulates characteristics of the problem at hand. Consequently, it is desirable to steer adaptation towards filters that lie on that manifold. In this paper, we propose a novel approach to learn the manifold of a set of impulse responses and subsequently employ that learned manifold in an adaptation algorithm for system identification. The presented approach is a practical adaptive filtering recipe for enforcing a data-driven search domain constraint, instead of using conventional constrained optimization methods. © 2023 IEEE.

关键词： adaptive filtering generative models Manifold learning variational autoencoder

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Monolith to Microservices: VAE-Based GNN Approach with Duplication Consideration 16

Monolith to Microservices: VAE-Based GNN Approach with Dupli...

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16th IEEE International Conference on Service-Oriented System Engineering (SOSE)

作者： Sooksatra, Korn Maharjan, Rokin Cerny, Tomas Baylor Univ Dept Comp Sci Waco TX 76798 USA

ISBN: (数字)9781665475341

ISBN: (纸本)9781665475341

With the rise of cloud computing, many applications have been implemented into microservices to fully utilize cloud computing for scalability and maintainability purposes. However, there are some traditional monolith applications that developers would like to partition into microservices. Unfortunately, it is difficult to find a solution when considering multiple factors (i.e., the strong dependency in each cluster and how often different microservices communicate with each other). Further, because we allow duplications of classes in multiple microservices to reduce the communications between them, the number of duplicated classes is also another important factor for maintainability. Therefore, we need to use machine learning algorithms to approximate a good solution due to the infeasibility of finding the optimal solution. We apply the variational autoencoder to extract features of classes and use the fuzzy c means to group the classes into microservices according to their extracted features. As a result, our approach outperforms the other baselines in some significant metrics. Also, when we allow duplication, we find that it is helpful in terms of reducing the overhead of communications between microservices.

关键词： Microservices Machine learning Clustering Graph neural networks variational autoencoder

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Generating Musical Sequences with a Given Emotion

Generating Musical Sequences with a Given Emotion

引用

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

作者： Grekow, Jacek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

ISBN: (纸本)9781665442077

This article presents the process of building a system generating music content of a specified emotion. As the emotion labels, four basic emotions: happy, angry, sad, relaxed, which correspond to the four quarters of Russell's model, were used. Conditional variational autoencoder using a recurrent neural network for sequence processing was used as a generative model. The obtained results in the form of the generated music examples with a specific emotion are convincing in their structure and sound. The generated examples were evaluated through comparison with the training set.

关键词： Generative models music generation music emotion variational autoencoder

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DVAE-SR: Denoiser variational Auto-Encoder and Super-Resolution to Counter Adversarial Attacks 13

DVAE-SR: Denoiser Variational Auto-Encoder and Super-Resolut...

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13th International Conference on Machine Vision

作者： Dardour, Omar Zaied, Mourad Radeva, Petia Univ Gabes Zrig Eddakhlania Tunisia Univ Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781510640412

Recently, adversarial examples become one of the most dangerous risks in deep learning, which affects applications of real world such as robotics, cyber-security and computer vision. In image classification, adversarial attacks showed the ability to fool classifiers with small imperceptible perturbations added to the input. In this paper, we present an efficient defense mechanism, we call DVAE-SR that combine variational autoencoder and super-resolution to eliminate adversarial perturbation from image input before feeding it to the CNN classifier. The DVAE-SR can successfully defend against both white-box and black-box attacks without retraining CNN classifier and it recovers better accuracy than Defense-GAN and Defense-VAE..

关键词： Adversarial attacks Adversarial defense variational autoencoder Super-resolution Deep learning Image classification Computer vision

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Using autoencoders to Generate Skeleton-Based Typography 12th

Using Autoencoders to Generate Skeleton-Based Typography

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12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) Held as Part of EvoStar Conference

作者： Parente, Jessica Goncalo, Luis Martins, Tiago Cunha, Joao Miguel Bicker, Joao Machado, Penousal Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9783031299551;9783031299568

Type Design is a domain that multiple times has profited from the emergence of new tools and technologies. The transformation of type from physical to digital, the dissemination of font design software and the adoption of web typography make type design better known and more accessible. This domain has received an even greater push with the increasing adoption of generative tools to create more diverse and experimental fonts. Nowadays, with the application of Machine Learning to various domains, typography has also been influenced by it. In this work, we produce a dataset by extracting letter skeletons from a collection of existing fonts. Then we trained a variational autoencoder and a Sketch Decoder to learn to create these skeletons that can be used to generate new ones by exploring the latent space. This process also allows us to control the style of the resulting skeletons and interpolate between different characters. Finally, we developed new glyphs by filling the generated skeletons based on the original letters' stroke width and showing some applications of the results.

关键词： Type Design variational autoencoder Skeleton-basis Typography

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