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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是231-240 订阅

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Semi-supervised Learning Using variational autoencoder - A Cluster Based Approach 9th

Semi-supervised Learning Using Variational Autoencoder - A C...

引用

9th International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Vengalil, Sunil Kumar Sinha, Neelam Int Inst Informat Technol Bangalore Bangalore Karnataka India

ISBN: (纸本)9783031126994;9783031127007

The successful application of deep neural networks for solving complex tasks like image classification, object detection and segmentation depends critically on the availability of large number of labelled training samples. To achieve good generalization for a reasonably complex model with about 60 million parameters, as in AlexNet, one needs about one million labelled training samples. In almost all practical applications, like natural image classification and segmentation, plenty of unlabelled samples are available but labelling these samples is a tedious manual task. We introduce a novel mechanism to automatically label all the samples in an unlabelled dataset. Starting with completely unlabelled dataset, an iterative algorithm incrementally assigns labels along with a confidence to all training samples. During each iteration, 10-30 new representative samples are generated in a latent space learned using a variational autoencoder and labels for these samples are obtained from a human expert. The proposed idea is demonstrated on MNIST dataset without using the labels provided in the dataset. At regular intervals of training, the low dimensional latent vectors are clustered and only cluster centers are annotated. The manual labels of cluster centers are propagated to other samples in the cluster based on the distance and a confidence function. The loss function in successive training is modified to incorporate the manual information provided. We run multiple experiments with different choices of clustering algorithm, confidence function and distance metric and compare the results. With GMM clustering, best classification accuracy of 93.9% was obtained on MNIST test images after 5 iterations.

关键词： Semi-supervised Learning variational autoencoder Active Learning Clustering

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A variational autoencoder for Model Independent Searches of New Physics at LHC 22nd

A Variational AutoEncoder for Model Independent Searches of ...

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

作者： Lavizzari, Giulia Boldrini, Giacomo Gennai, Simone Govoni, Pietro Univ Milano Bicocca ED U2 Piazza Scienza 3 I-20126 Milan Italy INFN Milano Bicocca ED U2 Piazza Scienza 3 I-20126 Milan Italy

ISBN: (纸本)9783031510229;9783031510236

We present a feasibility study for the use of a generative, probabilistic model, a variational autoencoder (VAE), to detect deviations from Standard Model (SM) physics in an electroweak process at the Large Hadron Collider (LHC). The new physics responsible for the anomalies is described through an Effective Field Theory (EFT) approach: the SM Lagrangian is Taylor-expanded and the higher order terms cause deviations in the kinematic distributions of the observables, and are thus identified by the model as anomalous contributions with respect to SM. Since the training of the model involves almost only SM events, the proposed strategy is largely independent from any assumption on the nature of the new physics signature. To test the proposed strategy we use parton level generations of Vector Boson Scattering (VBS) events at the LHC, assuming an integrated luminosity of 350 fb(-1).

关键词： variational autoencoder Anomaly Detection Vector Boson Scattering

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LSTMVAEF: Vivid Layout via LSTM-Based variational autoencoder Framework 16th

LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencode...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： He, Jie Wu, Xingjiao Hu, Wenxin Yang, Jing East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9783030863319

The lack of training data is still a challenge in the Document Layout Analysis task (DLA). Synthetic data is an effective way to tackle this challenge. In this paper, we propose an LSTM-based variational autoencoder framework (LSTMVAF) to synthesize layouts for DLA. Compared with the previous method, our method can generate more complicated layouts and only need training data from DLA without extra annotation. We use LSTM models as basic models to learn the potential representing of class and position information of elements within a page. It is worth mentioning that we design a weight adaptation strategy to help model train faster. The experiment shows our model can generate more vivid layouts that only need a few real document pages.

关键词： Document Layout Analysis variational autoencoder Document generation

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VDNet: An Image Dehazing Model Based on the variational autoencoder 36

VDNet: An Image Dehazing Model Based on the Variational Auto...

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36th Chinese Control and Decision Conference (CCDC)

作者： Sun, Qihang Yang, Zhihong Li, Ruifeng You, Yuyang Beijing Inst Technol Sch Automat Beijing Peoples R China Chinese Acad Med Sci Inst Med Plant Dev Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The hazy weather adversely affects the quality of images from acquisition devices. Traditional image enhancement technology has a poor visual effect. The atmospheric degradation methods combined with prior knowledge are required to restore the image under high-scene conditions. As a solution to these problems, we propose an end-to-end deep learning-based dehazing model VDNet based on the variational autoencoder. We design the flexible feature fusion module to prevent the degradation of low-level features during the model training process. In addition, VDNet introduces the perceptual loss, which effectively extracts the features in line with human perception, making the visual effect of dehazing images better. We use the public hazy image dataset RESIDE for model training and testing. Comparative experiments show that VDNet has excellent visual effects and objective indicators of dehazing with a reduced parameter count of 1.971M, which allows it to be used in devices with limited resources. VDNet achieves the result that the average PSNR is 36.98dB and SSIM is 0.9885.

关键词： Image dehazing variational autoencoder Flexible feature fusion Perceptual loss

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GF-VAE: A Flow-based variational autoencoder for Molecule Generation 21

GF-VAE: A Flow-based Variational Autoencoder for Molecule Ge...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ma, Changsheng Zhang, Xiangliang King Abdullah Univ Sci & Technol Jeddah Saudi Arabia

ISBN: (纸本)9781450384469

Generating novel molecules with desired properties is a fundamental problem in modern drug discovery. This is a challenging problem because it requires the optimization of the given objectives while obeying the rules of chemical valence. An effective approach is to incorporate the molecular graph with deep generative models. However, recent generative models with high-performance are still computationally expensive. In this paper, we propose GF-VAE, a flow-based variational autoencoder (VAE) model for molecular graph generation. Specifically, the model equips VAE a lightweight flow model as its decoder, in which, the encoder aims to accelerate the training process of the decoder, while the decoder in turns to optimize the performance of the encoder. Thanks to the invertibility of flow model, the generation process is easily accomplished by reversing the decoder. Additionally, the final generated molecules are processed by validity correction. Therefore, our GF-VAE inherits the advantages of both VAE and flow-based methods. We validate our model on molecule generation and reconstruction, smoothness of learned latent space, property optimization and constrained property optimization. The results show that our model achieves state-of-the-arts performance on these tasks. Moreover, the time performance of GF-VAE on two classical datasets can achieve 31.3% and 62.9% improvements separately than the state-of-the-art model.

关键词： Graph Generative Model variational autoencoder Normalizing Flow Molecular Generation Molecular property optimization

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A Surrogate Tiny Machine Learning Model of variational autoencoder For Real-Time Baseline Correction of Magnetometer Data

A Surrogate Tiny Machine Learning Model of Variational Autoe...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Siddique, Talha Mahmud, M. D. Shaad Univ New Hampshire Dept Elect & Comp Engn Durham NH 03824 USA

ISBN: (纸本)9798350371000;9798350370997

Magnetometers play a vital role in geophysics and space weather prediction applications by collecting terrestrial magnetic field data. They monitor solar-induced geomagnetic disturbances, providing essential insights into predicting space weather effects on technologies such as satellites, power grids, and communication networks. However, this data often contains inherent background noise, necessitating accurate baseline correction methods. Traditional correction techniques are robust but computationally demanding and unsuitable for real-time applications. Recent progress has investigated the utilization of Tiny Machine Learning (TinyML) to process magnetometer data in real time, especially when resources are limited. However, these edge-based ML solutions often lack the robustness of more computationally intensive probabilistic models, such as variational autoencoders (VAEs). This paper introduces a TinyML-VAE surrogate model designed for real-time magnetometer baseline correction. The surrogate model approximates an implemented VAE's performance while operating within the constrained resources of an edge device. The new model retains the VAE's uncertainty quantification capabilities by leveraging surrogate modeling techniques, ensuring robustness. Experimental outcomes have been displayed, illustrating a comparison between the performance of the TinyML-VAE and the benchmark established by the standard VAE.

关键词： Edge Machine Learning TinyML variational autoencoder Data Denoising Geomagnetic Data

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Joint QoT Estimation and Soft-Failure Localization using variational autoencoder

Joint QoT Estimation and Soft-Failure Localization using Var...

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27th International Conference on Optical Network Design and Modeling (ONDM)

作者： Kruse, Lars Eike Pachnicke, Stephan Christian Albrechts Univ Kiel Chair Commun Kiel Germany

ISBN: (纸本)9783903176546

We propose joint QoT estimation and soft-failure localization leveraging the latent space of a variational autoencoder trained on optical spectra. The framework shows F1-scores of 0.989 for soft-failure detection, 0.996 for identification and 0.908 for localization. The QoT estimator reaches an R2-score of 0.998 and a MAE of 0.17 dB.

关键词： Quality of transmission estimation failure localization variational autoencoder

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EFVAE: Efficient Federated variational autoencoder for Collaborative Filtering 24

EFVAE: Efficient Federated Variational Autoencoder for Colla...

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhang, Lu Rong, Qian Ding, Xuanang Li, Guohui Yuan, Ling Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798400704369

Federated recommender systems are used to address privacy issues in recommendations. Among them, FedVAE extends the representative non-linear recommendation method MultVAE. However, the bottleneck of FedVAE lies in its communication load during training, as the parameter volume of its first and last layers is correlated with the number of items. This leads to significant communication cost during the model's transmission phases (distribution and upload), making FedVAE's implementation extremely challenging. To address these challenges, we propose an Efficient Federated variational autoencoder for collaborative filtering, EFVAE, which core is the Federated Collaborative Importance Sampling (FCIS) method. FCIS reduces communication costs through a client-to-server collaborative sampling mechanism and provides satisfactory recommendation performance through dynamic multi-stage approximation of the decoding distribution. Extensive experiments and analyses on real-world datasets confirm that EFVAE significantly reduces communication costs by up to 94.51% while maintaining the recommendation performance. Moreover, its recommendation performance is better on sparse datasets, with improvements reaching up to 13.79%.

关键词： Collaborative Filtering Federated Learning Importance Sampling variational autoencoder

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Radar Image Reconstruction from Raw ADC Data using Parametric variational autoencoder with Domain Adaptation 25

Radar Image Reconstruction from Raw ADC Data using Parametri...

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25th International Conference on Pattern Recognition (ICPR)

作者： Stephan, Michael Stadelmayer, Thomas Santra, Avik Fischer, Georg Weigel, Robert Lurz, Fabian Infineon Technol AG Neubiberg Germany Friedrich Alexander Univ Erlangen Nuremberg Erlangen Germany

ISBN: (纸本)9781728188089

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.

关键词： Detection and Localization Parametric Deep Neural Network variational autoencoder Domain Adaptation

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Investigation of F0 conditioning and Fully Convolutional Networks in variational autoencoder based Voice Conversion 20

Investigation of F0 conditioning and Fully Convolutional Net...

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Interspeech Conference

作者： Huang, Wen-Chin Wu, Yi-Chiao Lo, Chen-Chou Tobing, Patrick Lumban Hayashi, Tomoki Kobayashi, Kazuhiro Toda, Tomoki Tsao, Yu Wang, Hsin-Min Acad Sinica Taipei Taiwan Nagoya Univ Nagoya Aichi Japan

In this work, we investigate the effectiveness of two techniques for improving variational autoencoder (VAE) based voice conversion (VC). First, we reconsider the relationship between vocoder features extracted using the high quality vocoders adopted in conventional VC systems, and hypothesize that the spectral features are in fact F0 dependent. Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent. To this end, we propose to utilize the F0 as an additional input of the decoder. The model can learn to disentangle the latent code from the F0 and thus generates converted F0 dependent converted features. Second, to better capture temporal dependencies of the spectral features and the F0 pattern, we replace the frame wise conversion structure in the original VAE based VC framework with a fully convolutional network structure. Our experiments demonstrate that the degree of disentanglement as well as the naturalness of the converted speech are indeed improved.

关键词： voice conversion variational autoencoder representation disentanglement

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