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检索条件"主题词=Variational autoencoder"

共 1554 条记录，以下是821-830 订阅

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Unsupervised SAR Representation Learning Improves Classification Performance 34

Unsupervised SAR Representation Learning Improves Classifica...

引用

Conference on Automatic Target Recognition XXXIV

作者： Vaughn, Nolan Sullivan, Bo Jaskie, Kristen Prime Solut Grp Inc Goodyear AZ 85338 USA

ISBN: (纸本)9781510673977;9781510673960

We compare the effectiveness of using a trained-from-scratch, unsupervised deep generative variational autoencoder (VAE) model as a solution to generic representation learning problems for Synthetic Aperture Radar (SAR) data as compared to the more common approach of using an Electric Optical (EO) transfer learning method. We find that a simple, unsupervised VAE training framework outperforms an EO transfer learning model at classification.

关键词： Representation Learning variational autoencoder VAE Transfer Learning Classification Synthetic Aperture Radar SAR

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PRVAE-VC2: Non-Parallel Voice Conversion by Distillation of Speech Representations 25

PRVAE-VC2: Non-Parallel Voice Conversion by Distillation of ...

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25th Interspeech Conference

作者： Tanaka, Kou Kameoka, Hirokazu Kaneko, Takuhiro Kondo, Yuto NTT Corp Tokyo Japan

This paper describes a knowledge distillation approach to non-parallel many-to-many voice conversion (VC) using self-supervised speech representation techniques: perturbation-resistant variational autoencoder (PRVAE) and a hidden-unit BERT (HuBERT). PRVAE has achieved a breakthrough by significantly improving non-streaming and low-latency streaming VC performances. However, a notable gap persists between the real target and converted speech, posing a continuing challenge. To narrow this gap, we present PRVAE-VC2, an improved version of PRVAE-VC that leverages rich and contextually informed representations derived from pre-trained HuBERT. Furthermore, we apply knowledge distillation techniques to make PRVAE-VC2 a streamable method, ensuring that the advantage of PRVAE-VC as a streamable VC is not compromised. Evaluation results demonstrate that our approaches effectively reduce discrepancies between the converted speech and the target. Audio samples can be accessed at our webpage (1).

关键词： Voice conversion speech representation knowledge distillation variational autoencoder self-supervised learning

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A HYBRID SURROGATE MODELING APPROACH FOR DATA REDUCTION AND DESIGN SPACE EXPLORATION OF TURBINE BLADES 69

A HYBRID SURROGATE MODELING APPROACH FOR DATA REDUCTION AND ...

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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Ali, Sazeed S. Yadav, Vikas S. Nouri, Behnam Ghani, Abdulla Siemens Energy Global GmbH & Co KG Berlin Germany TU Berlin Chair Data Anal & Modeling Turbulent Flows Berlin Germany

ISBN: (纸本)9780791888032

The conventional iterative geometry optimization process for turbine blades using computer aided engineering (CAE) simulations is both cost and time consuming and has limitations in terms of computational requirements and data management. A particularly problematic aspect is that the generated 3D simulation data of computational fluid dynamics (CFD) and finite element analysis (FEA) cannot be fully reused for future optimizations. The data for multiple geometry variations of the turbine blade can range from several hundred gigabytes to multiple terabytes. This leads to difficulties in storing and accessing them for a longer period. To address this challenge, we introduce a methodology based on machine learning models for data reduction and forecast of 3D surface field data. Specifically, we develop a convolutional variational autoencoder (VAE) that combines two models: the encoder and the decoder. The encoder transforms the input data into a representation of reduced dimensionality in a latent space, and the decoder reconstructs the input data from the given latent representation in its original space. The latent representation of the input data and the trained VAE together result in much smaller data amounts, solving the issue of data storage for future use. Furthermore, we train a fully connected feed-forward mutilayer perceptron (MLP) that learns the mapping of the geometry parameters, responsible for generating the variations, to the latent space. Thereby, we combine the MLP with the trained decoder of the VAE to our proposed multilayer perceptron variational autoencoder (MLP-VAE) hybrid neural network model. The MLP-VAE predicts the surface field data results for new and unseen blade geometry variations and generates their latent representations without any additional cost.

关键词： Surrogate Modeling Conjugate Heat Transfer Structural Mechanics Turbine Blade variational autoencoder Neural Networks

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Deep FAVIB: Deep Learning-Based Forward-Aware Quantization via Information Bottleneck Method 59

Deep FAVIB: Deep Learning-Based Forward-Aware Quantization v...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Hummert, Matthias Hassanpour, Shayan Wuebben, Dirk Dekorsy, Armin Univ Bremen Dept Commun Engn D-28359 Bremen Germany

ISBN: (纸本)9781728190549

We focus on a (generic) joint source-channel coding problem, appearing in a broad variety of real-world application. Explicitly, a noisy observation from a user/source signal should be compressed, ahead of getting forwarded over an error-prone and rate-limited channel to a remote processing unit. The design problem shall be formulated in a fashion that the impacts of the forward link are taken into account. Aligned with the Information Bottleneck (IB) method, we consider the Mutual Information (MI) as the fidelity criterion, and work out a data-driven approach to tackle the underlying design problem based upon a finite sample set. For that, we derive a tractable variational lower-bound of the objective functional, and present a general learning architecture which can be used to optimize the given lower-bound by standard training of the encoder and decoder Deep Neural Networks. This approach that is, principally, based upon the (generative) latent variable models, extends the concepts of variational autoencoder (VAE) and Deep variational Information Bottleneck (Deep VIB) for (remote) source coding to the context of joint source-channel coding. We validate the effectiveness of our approach by several numerical simulations over typical transmission scenarios.

关键词： Information bottleneck variational autoencoder deep learning quantization joint source-channel coding 6G

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Modeling variational Anchoring Effect for Recommender Systems 2

Modeling Variational Anchoring Effect for Recommender System...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Xiao, Yudi Zhang, Yingyi Li, Xianneng Dalian Univ Technol Sch Econ & Management Inst Adv Intelligence Dalian Peoples R China

ISBN: (纸本)9798350354102;9798350354096

Users generally have a tendency to rely on numerical information of recommendations presented on the web page when judging the recommended items, which refers to a classic psychological concept, anchoring effect. Learning users' psychology from explicit behaviors has been widely applied in RS and performs well on capturing user preferences and guiding the prediction tasks of recommendations. Recent studies have empirically proven that the anchoring effect can mislead users to click/purchase items that are not liked in principle, which will bring bias and noise to behavior data. However, vast majority of existing recommendation algorithms trained on behavior data ignore the anchoring bias, which results in suboptimal recommendations. In this paper, we propose a novel method named variational Anchoring Effect Encoder (VAEE) to model the anchoring effect and mitigate the anchoring bias for recommender systems. The proposed method mainly includes two steps: 1) User Anchoring Effect Module which aims to reconstruct the unanchored user preferences with a variational autoencoder (VAE)-based deep structure, and 2) User Anchoring Debias Module that generate the recommendation results with the reconstructed unbiased user representations. Extensive experiments on real-world datasets are conducted to demonstrate that reducing anchoring effect can bring particular improvement in AUC and the proposed VAEE is attachable to most existing recommendation models. We also compare the recommendation quality when using different anchoring feature subsets, which indicates that the learned representation of anchoring effect is authentic and truly effective to restore users' true preferences.

关键词： anchoring bias anchoring effect recommender systems variational autoencoder

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The Temporal Random Scene Generation Method Considering Source-Load Correlation 7

The Temporal Random Scene Generation Method Considering Sour...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Zhou, Hao Hong, Lucheng Liu, Hui Gu, Jie Zhu, Zihan Ren, Xiaofeng Southeast Univ Jiangsu Prov Key Lab Smart Grid Technol & Equipme Nanjing Peoples R China Jiangsu Elect Power Supply Co Xuzhou Elect Power Supply Branch Off Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9798350375794;9798350375800

With the continuous increase in the penetration rate of renewable distribution generation (RDG) and the integration of various flexible controllable loads in new distribution systems, the uncertainty from both generation and demand sides poses significant challenges to the planning and scheduling of active distribution networks (AND). In response to this issue, this paper proposes a novel distribution network joint scenario generation method incorporating source-load correlation variational autoencoder (VAE), accompanied by the design of appropriate encoder and decoder network architectures. This method intrinsically integrates a position encoding module into the conventional VAE structure, employs temporal convolutional networks (TCN) for extracting correlation features of multi-dimensional stochastic variables related to source and load. Subsequently, simulation analysis is conducted based on actual photovoltaic and load data. The results demonstrate the effectiveness of the proposed model and method in learning the stochasticity and correlation of multi-dimensional time series variables, thus providing a reliable scenario modeling approach for planning and long-term operation.

关键词： New Distribution System renewable distribution generation Scenario Generation variational autoencoder

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Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches 3rd

Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchm...

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3rd Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention (DGM4MICCAI) at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Hassanaly, Ravi Brianceau, Camille Colliot, Olivier Burgos, Ninon Sorbonne Univ Hop Pitie Salpetriere AP HP Inst CerveauParis Brain InstICMCNRSInsermInr F-75013 Paris France

ISBN: (纸本)9783031537660;9783031537677

The use of deep generative models for unsupervised anomaly detection is an area of research that has gained interest in recent years in the field of medical imaging. Among all the existing models, the variational autoencoder (VAE) has proven to be efficient while remaining simple to use. Much research to improve the original method has been achieved in the computer vision literature, but rarely translated to medical imaging applications. To fill this gap, we propose a benchmark of fifteen variants of VAE that we compare with a vanilla autoencoder and VAE for a neuroimaging use case relying on a simulation-based evaluation framework. The use case is the detection of anomalies related to Alzheimer's disease and other dementias in 3D FDG PET. We showthat among the fifteen VAE variants tested, nine lead to a good reconstruction accuracy and are able to generate healthy-looking images. This indicates that many approaches developed for computer vision applications can generalize to the unsupervised detection of anomalies of various shapes, intensities and locations in 3DFDGPET. However, these models do not outperform the vanilla autoencoder and VAE.

关键词： variational autoencoder Deep generative models Unsupervised anomaly detection PET Alzheimer's disease

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Can I Trust My Anomaly Detection System? A Case Study Based on Explainable AI 2nd

Can I Trust My Anomaly Detection System? A Case Study Based ...

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2nd World Conference on Explainable Artificial Intelligence (xAI)

作者： Rashid, Muhammad Amparorel, Elvio Ferrari, Enrico Verda, Damiano Univ Torino Comp Sci Dept Cso Svizzera 185 I-10149 Turin Italy Rulex Innovat Labs Via Felice Romani 9 I-16122 Genoa Italy

ISBN: (纸本)9783031638022;9783031638039

Generative models based on variational autoencoders are a popular technique for detecting anomalies in images in a semi-supervised context. A common approach employs the anomaly score to detect the presence of anomalies, and it is known to reach high level of accuracy on benchmark datasets. However, since anomaly scores are computed from reconstruction disparities, they often obscure the detection of various spurious features, raising concerns regarding their actual efficacy. This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models through the use of eXplainable AI methods. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences. In our case study we discovered that, in many cases, samples are detected as anomalous for the wrong or misleading factors.

关键词： anomaly detection variational autoencoder eXplainable AI

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Action Conditioned Attention Encoder-Decoder and Discriminator for Human Motion Generation 1

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5th International Conference on Deep Learning Theory and Applications (DeLTA)

作者： Bandi, Chaitanya Thomas, Ulrike Tech Univ Chemnitz Chemnitz Germany

ISBN: (数字)9783031666940

ISBN: (纸本)9783031666933;9783031666940

We present a CVAE-GAN-based architecture for human motion generation with an action-conditioned variational autoencoder and a generative discriminator. In this work, we focus on generating more accurate actions performed by a single person using a conditioned generative model. The primary motivation for this work comes from human-robot collaboration scenarios where a person interacts with the robot using human actions. Our approach consists of a self-attention-based conditional variational autoencoder for reconstructions and a graph network-based discriminator for realistic human motion quality. We evaluate our network on three open-source datasets known as HumanAct12, NTU 120 RGB+D, and Actions in Supermarket Dataset. The extensive experiments show that the presented approach works for various human motions and input representations, such as the SMPL pose parameters, trajectory data, and skeleton joints. We achieve higher accuracy compared to the state-of-the-art methods on all three datasets.

关键词： Human motion generation Human actions variational autoencoder

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Compositionality and Generalization in Emergent Communication using Metropolis-Hastings Naming Game

Compositionality and Generalization in Emergent Communicatio...

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IEEE International Conference on Development and Learning (ICDL)

作者： Le Hoang, Nguyen Matsui, Yuta Hagiwara, Yoshinobu Ritsumeikan Univ Grad Sch Info Sci & Engn Osaka Japan Ritsumeikan Univ Res Org Sci & Tech Osaka Shiga Japan

ISBN: (纸本)9798350348569;9798350348552

This study investigates the emergence of compositionality and generalization within Emergent Communication (EmCom) systems, focusing on emergent language using the Metropolis-Hastings naming game (MHNG). Although the MHNG has been used in previous EmCom research, this is the first study to explore compositionality, the ability to construct complex expressions by combining simpler elements, and generalization, the ability to apply learned patterns to new situations, in emergent language within this language game. We introduce the novel Inter-VAE+VAE model, which equips each agent with dual variational autoencoders (VAEs), specifically designed for cognitive tasks that mirror human perception and language processing. This model, facilitating predictive coding, allows agents to refine their world models through collective experiences, a process rooted in the collective predictive coding hypothesis and differing from isolated learning approaches. Our model was evaluated against baseline models, including the beta-VAE and ss-TCVAE, and was further compared with implementations of the Lewis signaling game using the dSprites and 3Dshapes datasets. The results from these evaluations indicate an improvement in agent communication within the MH naming game and underscore the model's potential in replicating key aspects of human language, particularly compositionality and generalization, in artificial systems.

关键词： emergent communication symbol emergence metropolis-hastings naming game compositionality generalization variational autoencoder

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