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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是1231-1240 订阅

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Dynamic Narrowing of VAE Bottlenecks Using GECO and L₀ Regularization

Dynamic Narrowing of VAE Bottlenecks Using GECO and <i>L</i>...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： De Boom, Cedric Wauthier, Samuel Verbelen, Tim Dhoedt, Bart Univ Ghent IDLab Dept Informat Technol IMEC Technologiepk Zwijnaarde 126 B-9052 Ghent Belgium

ISBN: (纸本)9780738133669

When designing variational autoencoders (VAEs) or other types of latent space models, the dimensionality of the latent space is typically defined upfront. In this process, it is possible that the number of dimensions is under- or overprovisioned for the application at hand. In case the dimensionality is not predefined, this parameter is usually determined using time- and resource-consuming cross-validation. For these reasons we have developed a technique to shrink the latent space dimensionality of VAEs automatically and on-the-fly during training using Generalized ELBO with Constrained Optimization (GECO) and the L-0-Augment-REINFORCE-Merge (L-0-ARM) gradient estimator. The GECO optimizer ensures that we are not violating a predefined upper bound on the reconstruction error. This paper presents the algorithmic details of our method along with experimental results on five different datasets. We find that our training procedure is stable and that the latent space can be pruned effectively without violating the GECO constraints.

关键词： variational autoencoder VAE latent space reduction GECO L-0 regularization ARM

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Winding System for Fishing Simulator with Real Rod 20

Winding System for Fishing Simulator with Real Rod

引用

20th International Symposium on Communications and Information Technologies (ISCIT)

作者： Honda, Yoshimichi Kawamura, Arata Kyoto Sangyo Univ Div Frontier Informat Kyoto Japan

ISBN: (纸本)9781665449588

To enjoy fishing indoors, we study a hardware-type fishing simulator that employs a real fishing rod. In this paper, as the first step of our research, we develop a pull force acquisition system and a winding system that consists of a motor, reel, and controller. The pull force acquisition system obtains an actual fish pull force. The time variation of the pull force represents the pull pattern of the fish. We show that the winding system can reproduce a pull pattern similar to the original pull pattern obtained at the pull force acquisition system. A lot of pull pattern of fish has to be acquired to represent a specific pull pattern to the fish species. It is inefficient to obtain them with fieldwork. We use a variational autoencoder (VAE) to generate multiple pull patterns similar to the original pull pattern. Here we assume that the fish species-specific pull pattern maintains its rough shape of movements. Simulation results showed that VAE generated multiple pull patterns roughly maintaining the original shape.

关键词： fishing fishing-simulator pull of a fish real fishing rod variational autoencoder

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IMPORTANCE SAMPLING BASED FEDERATED UNSUPERVISED REPRESENTATION LEARNING 49

IMPORTANCE SAMPLING BASED FEDERATED UNSUPERVISED REPRESENTAT...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shaht, Nazreen Goyalt, Prachi Prasad, Ranjitha IIITD ECE Dept Delhi India

ISBN: (纸本)9798350344868;9798350344851

The use of AI has led to the era of pervasive intelligence, marked by a proliferation of smart devices in our daily lives. Federated Learning (FL) enables machine learning at the edge without having to share user-specific private data with an untrusted third party. Conventional FL techniques are supervised learning methods, where a fundamental challenge is to ensure that data is reliably annotated at the edge. Another approach is to obtain rich and informative representations of unlabeled data, which is suitable for downstream tasks. We propose a novel IS-FedVAE framework where we use importance sampling to federate a global VAE framework, allowing us to learn the global latent space distribution using local latent space distributions at the edge. We evaluate the representation in a stand-alone manner using linear probe, where we freeze the backbone representation and measure the accuracy of a downstream classifier. Furthermore, we demonstrate that IS-FedVAE outperforms state-of-the-art unsupervised FL learning baselines. We also show that IS-FedVAE is insensitive to varying levels of heterogeneity, scalable to varying numbers of clients, and robust to the changing number of local epochs.

关键词： variational autoencoder representation learning federated learning importance sampling.

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VS-Net: Multiscale Spatiotemporal Features for Lightweight Video Salient Document Detection 34

VS-Net: Multiscale Spatiotemporal Features for Lightweight V...

引用

34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Singh, Hemraj Verma, Mridula Cheruku, Ramalingaswamy Natl Inst Technol Warangal Dept Comp Sci & Engg Warangal Telangana India Inst Dev & Res Banking Technol Hyderabad Hyderabad Telangana India

ISBN: (纸本)9798350397444

Video Salient Document Detection (VSDD) is an essential task of practical computer vision, which aims to highlight visually salient document regions in video frames. Previous techniques for VSDD focus on learning features without considering the cooperation among and across the appearance and motion cues and thus fail to perform in practical scenarios. Moreover, most of the previous techniques demand high computational resources, which limits the usage of such systems in resource-constrained settings. To handle these issues, we propose VS-Net, which captures multi-scale spatiotemporal information with the help of dilated depth-wise separable convolution and Approximation Rank Pooling. VS-Net extracts the key features locally from each frame across embedding sub-spaces and forwards the features between adjacent and parallel nodes, enhancing model performance globally. Our model generates saliency maps considering both the background and foreground simultaneously, making it perform better in challenging scenarios. The immense experiments regulated on the benchmark MIDV-500 dataset show that the VS-Net model outperforms state-of-the-art approaches in both time and robustness measures.

关键词： Separable convolution Approximation Rank Pooling variational autoencoder Multi-scale features

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variational Deep Semantic Hashing for Text Documents 17

Variational Deep Semantic Hashing for Text Documents

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40th International ACM SIGIR conference on research and development in Information Retrieval

作者： Chaidaroon, Suthee Fang, Yi Santa Clara Univ Dept Comp Engn Santa Clara CA 95053 USA

ISBN: (纸本)9781450350228

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.

关键词： Semantic hashing variational autoencoder Deep learning

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Cross-utterance Conditioned Coherent Speech Editing 24

Cross-utterance Conditioned Coherent Speech Editing

引用

Interspeech Conference

作者： Yu, Cheng Li, Yang Zu, Weiqin Sun, Fanglei Tian, Zheng Wang, Jun ShanghaiTech Univ Shanghai Peoples R China Univ Manchester Manchester Lancs England UCL London England

Text-based speech editing systems are developed to enable users to modify speech based on the transcript. Existing state-of-the-art editing systems based on neural networks do partial inferences with no exception, that is, only generate new words that need to be replaced or inserted. This manner usually leads to the prosody of the edited part being inconsistent with the surrounding speech and a failure to handle the alteration of intonation. To address these problems, we propose a cross-utterance conditioned coherent speech editing system, that first does the entire reasoning at the inference time. Our proposed system can generate speech by utilizing speaker information, context, acoustic features, and the mel-spectrogram from the original audio. Experiments conducted on subjective and objective metrics demonstrate that our approach outperforms the baseline on various editing operations regarding naturalness and prosody consistency.

关键词： speech editing variational autoencoder

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Statistical Modeling of Pulmonary Vasculatures with Topological Priors in CT Volumes 4th

Statistical Modeling of Pulmonary Vasculatures with Topologi...

引用

4th Int Workshop on Interpretabil of Machine Intelligence in Med Image Comp (iMIMIC) / 1st Int Workshop on Topol Data Analysis and Its Applicat for Med Data (TDA4MedicalData) at 24th Int Conf on Med Image Comp and Comp Assisted Intervent (MICCAI)

作者： Saeki, Yuki Saito, Atsushi Cousty, Jean Kenmochi, Yukiko Shimizu, Akinobu Tokyo Univ Agr & Technol Tokyo Japan Univ Gustave Eiffel CNRS ESIEE Paris LIGM Marne La Vallee France

ISBN: (纸本)9783030874445;9783030874438

A statistical appearance model of blood vessels based on variational autoencoder (VAE) is well adapted to image intensity variations. However, images reconstructed with such a statistical model may have topological defects, such as loss of bifurcation and creation of undesired hole. In order to build a 3D anatomical model of blood vessels, we incorporate topological prior into the statistical modeling. Qualitative and quantitative results on 2567 real CT volume patches and on 10000 artificial ones show the efficiency of the proposed framework.

关键词： Statistical model Topological data analysis variational autoencoder Vasculature

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ECG Data Modeling and Analyzing via Deep Representation Learning and Nonparametric Hidden Markov Models 21

ECG Data Modeling and Analyzing via Deep Representation Lear...

引用

44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Zhu, Jiaojiao Fan, Wentao Huaqiao Univ Dept Comp Sci & Technol Xiamen Fujian Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Xiamen Fujian Peoples R China

ISBN: (纸本)9781450380379

In modern clinical medicine, electrocardiogram (ECG) is a common diagnosis technique of cardiovascular diseases. The purpose of this paper is to propose a novel model-based clustering approach for analyzing ECG data. Our approach is composed of two modules: representation learning and ECG data clustering. In the module of representation learning, a deep generative model referred to as the hyperspherical variational recurrent autoencoder (HVRAE) is developed to extract the representation of observed ECG data, based on the variational autoencoder (VAE) with long short-term memory (LSTM) networks. In the module of ECG data clustering, we develop a nonparametric hidden Markov model (NHMM) based on Dirichlet process in which the number of hidden states is inferred automatically during the learning process. Moreover, the emission density of each hidden state of our NHMM follows a mixture of von Mises-Fisher (VMF) distributions which have better capability for modeling ECG representations than other commonly used distributions (such as the Gaussian distribution). To learn the proposed VMF-based NHMM, we theoretically develop an effective learning algorithm based on variational Bayes. The merits of our model-based clustering approach for analyzing ECG data are verified through experiments on publicly available ECG data sets.

关键词： ECG data clustering representation learning variational autoencoder HMM variational Bayes

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Unsupervised Trace-SPC anomaly detection solution based on VAE model 35

Unsupervised Trace-SPC anomaly detection solution based on V...

引用

35th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

作者： Wang, Xiaofei Jiang, Hui Semitronix Corp Hangzhou Peoples R China

ISBN: (纸本)9798350384567;9798350384550

Wafer manufacturing is a complex, expensive, and time-consuming process that involves multiple steps. By closely monitoring the corresponding variations in process parameters, potential anomalies and faults in the process can be timely detected and identified, effectively improving equipment utilization and product yield. However, the anomaly detection methods currently in use rely on extracting feature values from sensor data and analyzing the statistical information of these features. This approach fails to identify complex anomaly patterns, and constructing meaningful feature values can be challenging. In this paper, we propose an unsupervised anomaly detection solution based on the variational autoencoder (VAE) model. We optimized the training strategy of the VAE model to address the issue of posterior collapse. By reconstructing the sensor data using the VAE model, we can effectively convert anomalous patterns into normal ones. The experimental results confirm the effectiveness of the model across various sensor datasets, demonstrating the capability of the proposed solution to accurately identify abnormal patterns within the sensor data.

关键词： wafer manufacturing data reconstruction Trace-SPC anomaly detection variational autoencoder

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Personalization Disentanglement for Federated Learning

Personalization Disentanglement for Federated Learning

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yan, Peng Long, Guodong Univ Technol Sydney Fac Engn & IT Sydney NSW Australia

ISBN: (纸本)9781665468916

Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This paper addresses PFL via explicit disentangling latent representations into two parts to capture the shared knowledge and client-specific personalization, which leads to more reliable and effective PFL. The disentanglement is achieved by a novel Federated Dual variational autoencoder (FedDVA), which employs two encoders to infer the two types of representations. FedDVA can produce a better understanding of the trade-off between global knowledge sharing and local personalization in PFL. Moreover, it can be integrated with existing FL methods and turn them into personalized models for heterogeneous downstream tasks. Extensive experiments validate the advantages caused by disentanglement and show that models trained with disentangled representations substantially outperform those vanilla methods.

关键词： Federated Learning Disentanglement variational autoencoder

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