检索结果-内蒙古大学图书馆

IEEE International Conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Cheret, Laurent Yves Emile Ramos de Oliveira, Thiago Eustaquio Alves Lakehead Univ Dept Comp Sci Thunder Bay ON Canada

ISBN: (数字)9781665483568

ISBN: (纸本)9781665483568

Transfer Learning (TL) plays a vital role in image classification systems based on Deep Convolutional Neural Networks (DCNNs). Systems employing such technique may be susceptible to distortions on images, motivating the development of robust DCNNs capable of facing these problems. Unfortunately, changes in the architecture of DCNNs are sometimes specific to a kind of distortion and result in models that need to be retrained from scratch. This work proposes the use of autoencoders as intermediaries between pre-trained DCNNs and classifiers, delegating the denoising task to this architecture trained to encode feature maps. The classifiers are then trained to map the inputs from the autoencoder latent spaces to their respective classes. Models employing this approach achieved 3% to 4% increase in accuracy and 50% to 70% reduction in loss on the CIFAR10 and CIFAR100 datasets. The results also showed an up to 80% reduction in loss and up to 15% increase in accuracy for images with unseen distortions compared to the classical TL approach. This work improves classification results and increases robustness to distortions in a straightforward manner.

关键词： Image Classification variational autoencoder Transfer Learning Deep Convolutional Networks Impulse Noise Gaussian JPEG Compression

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Automatic Phenotyping by a Seed-guided Topic Model 22

Automatic Phenotyping by a Seed-guided Topic Model

引用

28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Song, Ziyang Hu, Yuanyi Verma, Aman Buckeridge, David L. Li, Yue McGill Univ Sch Comp Sci Montreal PQ Canada McGill Univ Dept Math & Stat Montreal PQ Canada McGill Univ Sch Populat & Global Hlth Montreal PQ Canada

ISBN: (纸本)9781450393850

Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand and predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary;(2) we model age-dependent disease progression as Markovian dynamic topic priors;(3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub.

关键词： topic modeling variational autoencoder electronic health records predictive healthcare

来源：评论

学校读者我要写书评

暂无评论

Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape Features 7th

Prediction of MGMT Methylation Status of Glioblastoma Using ...

引用

7th International Brain Lesion Workshop (BrainLes)

作者： Palsson, Sveinn Cerri, Stefano Van Leemput, Koen Tech Univ Denmark Dept Hlth Technol Lyngby Denmark Harvard Med Sch Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Boston MA 02115 USA

ISBN: (纸本)9783031090028;9783031090011

In this paper we propose a method for predicting the status of MGMT promoter methylation in high-grade gliomas. From the available MR images, we segment the tumor using deep convolutional neural networks and extract both radiomic features and shape features learned by a variational autoencoder. We implemented a standard machine learning workflow to obtain predictions, consisting of feature selection followed by training of a random forest classification model. We trained and evaluated our method on the RSNA-ASNR-MICCAI BraTS 2021 challenge dataset and submitted our predictions to the challenge.

关键词： MGMT prediction Radiomics Deep learning Glioblastoma variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

GENERALIZED ZERO-SHOT LEARNING USING CONDITIONAL WASSERSTEIN autoencoder 47

GENERALIZED ZERO-SHOT LEARNING USING CONDITIONAL WASSERSTEIN...

引用

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kim, Junhan Shim, Byonghyo Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781665405409

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes. Conventionally, conditional generative models have been employed to generate training data for unseen classes from the attribute. In this paper, we propose a new conditional generative model that improves the GZSL performance greatly. In a nutshell, the proposed model, called conditional Wasserstein autoencoder (CWAE), minimizes the Wasserstein distance between the real and generated image feature distributions using an encoder-decoder architecture. From the extensive experiments on various benchmark datasets, we show that the proposed CWAE outperforms conventional generative models in terms of the GZSL classification performance.

关键词： Generalized zero-shot learning generative model generative adversarial network variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Smart Meter Data Anomaly Detection Using variational Recurrent autoencoders with Attention 4th

Smart Meter Data Anomaly Detection Using Variational Recurre...

引用

4th International Conference on Intelligent Technologies and Applications (INTAP)

作者： Dai, Wenjing Liu, Xiufeng Heller, Alfred Nielsen, Per Sieverts Tech Univ Denmark Dept Technol Management & Econ DK-2800 Lyngby Denmark Niras OStre Havnegade 12 DK-9000 Aalborg Denmark

ISBN: (纸本)9783031105258;9783031105241

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a variational Recurrent autoencoder with attention mechanism. with "dirty" data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.

关键词： Anomaly detection variational autoencoder Smart meter data Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Diverse and Adjustable Versatile Image Enhancer

引用

IEEE ACCESS 2021年 9卷 80883-80896页

作者： Kim, Woojae Anh-Duc Nguyen Kim, Jinwoo Kim, Jongyoo Oh, Heeseok Lee, Sanghoon Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Microsoft Res Asia Beijing 100080 Peoples R China Hansung Univ Div IT Convergence Engn Seoul 02876 South Korea Yonsei Univ Coll Med Dept Radiol Seoul 120749 South Korea

Enhancing the quality of photographs is a highly subjective process and depends on users' preferences. Hence, it is often more desired to let users choose their own best from a set of diverse and adjustable enhanced images with astounding quality. However, a system that can satisfy this requirement has not yet been established. While classical algorithms blindly enhance an image by filtering, recent intelligent enhancement systems can only do it with limited styles through learning from a set of single expert-retouched (ER) images. To fill this void, we propose a novel framework, Diverse and adjustable Versatile Image Enhancer (DaVIE), that learns from multiple ER images simultaneously. Thereby, it can output diverse results without being bound to a specific enhancement style while allowing users to freely adjust the level of enhancement. For ease of diversity, we adopt a variational auto-encoder (VAE) that learns stochastic distribution of enhancement styles. By using the VAE, the proposed model provides diversely enhanced images. To establish better control in terms of enhancement level, we propose a more general form of adaptive instance normalization and loss functions, which can afford even extreme image editing. Through rigorous experiments, we demonstrate that the proposed DaVIE framework yields visually pleasing and diverse results. We also show the proposed model quantitatively outperforms existing methods on the MIT-Adobe-5K dataset. Furthermore, through a strict user-study, we show that the users consider the qualities of ER images and machine-retouched images to be similar, with about 35% selection probability for DaVIE enhanced images.

关键词： Image enhancement Erbium Training Generative adversarial networks Tools Licenses Aerospace electronics Diverse image enhancement adjustable learning automatic photo enhancement variational autoencoder adaptive-instance normalization

来源：评论

学校读者我要写书评

暂无评论

Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning

引用

MEDICAL PHYSICS 2021年第9期48卷 4730-4742页

作者： Zhang, Tianfang Bokrantz, Rasmus Olsson, Jimmy KTH Royal Inst Technol Dept Math SE-10044 Stockholm Sweden RaySearch Labs Stockholm Sweden

Purpose We propose a general framework for quantifying predictive uncertainties of dose-related quantities and leveraging this information in a dose mimicking problem in the context of automated radiation therapy treatment planning. Methods A three-step pipeline, comprising feature extraction, dose statistic prediction and dose mimicking, is employed. In particular, the features are produced by a convolutional variational autoencoder and used as inputs in a previously developed nonparametric Bayesian statistical method, estimating the multivariate predictive distribution of a collection of predefined dose statistics. Specially developed objective functions are then used to construct a probabilistic dose mimicking problem based on the produced distributions, creating deliverable treatment plans. Results The numerical experiments are performed using a dataset of 94 retrospective treatment plans of prostate cancer patients. We show that the features extracted by the variational autoencoder capture geometric information of substantial relevance to the dose statistic prediction problem and are related to dose statistics in a more regularized fashion than hand-crafted features. The estimated predictive distributions are reasonable and outperforms a non-input-dependent benchmark method, and the deliverable plans produced by the probabilistic dose mimicking agree better with their clinical counterparts than for a non-probabilistic formulation. Conclusions We demonstrate that prediction of dose-related quantities may be extended to include uncertainty estimation and that such probabilistic information may be leveraged in a dose mimicking problem. The treatment plans produced by the proposed pipeline resemble their original counterparts well, illustrating the merits of a holistic approach to automated planning based on probabilistic modeling.

关键词： dose mimicking dose-volume histogram prediction knowledge-based planning mixture-of-experts uncertainty modeling variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

VAE-based Deep SVDD for anomaly detection

引用

NEUROCOMPUTING 2021年 453卷 131-140页

作者： Zhou, Yu Liang, Xiaomin Zhang, Wei Zhang, Linrang Song, Xing Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Anomaly detection is an essential task for different fields in the real world. The imbalanced data and lack of labels make the task challenging. Deep learning models based on autoencoder (AE) have been applied to address the above difficulties successfully. However, in these AE-based deep methods, the AE-based model's optimization and the anomaly detector design are separated. Therefore, the latent representations in AE are less relevant for the anomaly detection task, which reduces the accuracy of anomaly detec-tion. A deep support vector data description based on variational autoencoder (Deep SVDD-VAE) is proposed in this paper to solve this problem. In the proposed model, VAE is used to reconstruct the input instances, while a spherical discriminative boundary is learned with the latent representations simulta-neously based on SVDD. Unlike existing AE-based methods, we seek the model parameters via the joint optimization of VAE and SVDD, which ensures the separability of the latent representations. Experimental results on MNIST, CIFAR-10, and GTSRB datasets show the effectiveness of Deep SVDD-VAE. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Anomaly detection variational autoencoder Deep support vector data description

来源：评论

学校读者我要写书评

暂无评论

Robustness Analysis of Deep Learning Models for Population Synthesis

引用

Transportation Research Procedia 2025年 82卷 3790-3806页

作者： Daniel Opoku Mensah Godwin Badu-Marfo Bilal Farooq Laboratory of Innovations in Transportation (LiTrans) Toronto Metropolitan University Toronto Canada

Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been proposed, but their performance is only tested on a single cross-sectional sample. The implementation of population synthesis on single datasets is seen as a drawback that needs further studies to explore the robustness of the models on multiple datasets. While comparing with the real data can increase trust and interpretability of the models, techniques to evaluate deep generative models’ robustness for population synthesis remain underexplored. In this study, we present bootstrap confidence interval for the deep generative models, an approach that computes efficient confidence intervals for mean errors predictions to evaluate the robustness of the models to multiple datasets. Specifically, we adopt the tabular-based Composite Travel Generative Adversarial Network (CTGAN) and variational autoencoder (VAE), to estimate the distribution of the population, by generating agents that have tabular data using several samples over time from the same study area. The models are implemented on multiple travel diaries of Montréal Origin-Destination Survey of 2008, 2013, and 2018 and compare the predictive performance under varying sample sizes from multiple surveys. Results show that the predictive errors of CTGAN have narrower confidence intervals indicating its robustness to multiple datasets of the varying sample sizes when compared to VAE. Again, the evaluation of model robustness against varying sample size shows a minimal decrease in model performance with decrease in sample size. This study directly supports agent-based modelling by enabling finer synthetic generation of populations in a reliable environment.

关键词： Generative Adversarial Networks variational autoencoder population synthesis robustness

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：