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检索条件"主题词=Conditional Variational Autoencoder"

共 109 条记录，以下是81-90 订阅

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Uncertainty Inspired Underwater Image Enhancement 17th

Uncertainty Inspired Underwater Image Enhancement

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17th European Conference on Computer Vision (ECCV)

作者： Fu, Zhenqi Wang, Wu Huang, Yue Ding, Xinghao Ma, Kai-Kuang Xiamen Univ Xiamen 361005 Fujian Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9783031197963;9783031197970

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we resolve UIE into distribution estimation and consensus process. We present a novel probabilistic network to learn the enhancement distribution of degraded underwater images. Specifically, we combine conditional variational autoencoder with adaptive instance normalization to construct the enhancement distribution. After that, we adopt a consensus process to predict a deterministic result based on a set of samples from the distribution. By learning the enhancement distribution, our method can cope with the bias introduced in the reference map labeling to some extent. Additionally, the consensus process is useful to capture a robust and stable result. We examined the proposed method on two widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our approach enables sampling possible enhancement predictions. Meanwhile, the consensus estimate yields competitive performance compared with state-of-theart UIE methods. Code available at https://***/zhenqifu/PUIENet.

关键词： Underwater image enhancement Deep learning Probabilistic network Adaptive instance normalization conditional variational autoencoder

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CVaDeS: A conditional variational Deep Survival Model for Survival Analysis 23

CVaDeS: A Conditional Variational Deep Survival Model for Su...

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23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Luo, Jinyuan Xiao, Zikai Shi, Wen Xie, Linhai Yang, Hong Yin, Xiaoxia Zhang, Yanchun Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China Natl Ctr Prot Sci Beijing State Key Lab Prote Beijing Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350307887

Transeriptome-hased survival modeling is a critical yet complicated task in cancer treatment due to the strong association between the prognosis of a patient and the progression of tumors based on heterogeneous molecular mechanisms. An effective prognosis model needs to consider the uncertainty that naturally stems from the high molecular heterogeneity together with numerous unforeseeable factors related to prognosis, and requires a perfect match between the selected covariates and the modeling approach of the joint distribution between covariates and right-censored survival time. Unfortunately, these necessary factors have not been fully considered in existing survival models. In this paper, we propose a novel conditional variational deep survival model (CVaDeS for short) for survival analysis. CVaDeS accurately predicts the risk values of patients and effectively distinguishes high -risk and low-risk individuals. Also, the proposed model CVaDeS can be effectively combined with Cox feature selection for survival analysis. Extensive experimental results demonstrate that the combination of Cox feature selection with CVaDeS significantly improves the predictive performance of the model, surpassing baseline models.

关键词： Survival Analysis Gene Screening conditional variational autoencoder

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SampleFix: Learning to Generate Functionally Diverse Fixes 21st

SampleFix: Learning to Generate Functionally Diverse Fixes

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Hajipour, Hossein Bhattacharyya, Apratim Staicu, Cristian-Alexandru Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9783030937331;9783030937324

Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning, and NLP have rekindled the hope to eventually fully automate the process of repairing programs. However, previous approaches that aim to predict a single fix are prone to fail due to uncertainty about the true intend of the programmer. Therefore, we propose a generative model that learns a distribution over potential fixes. Our model is formulated as a deep conditional variational autoencoder that can efficiently sample fixes for a given erroneous program. In order to ensure diverse solutions, we propose a novel regularizer that encourages diversity over a semantic embedding space. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to 45% of the erroneous programs. We additionally show that for the 65% of the repaired programs, our approach was able to generate multiple programs with diverse functionalities.

关键词： Program repair Generative models conditional variational autoencoder

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variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery 22nd

Variational Shape Completion for Virtual Planning of Jaw Rec...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Abdi, Amir H. Pesteie, Mehran Prisman, Eitan Abolmaesumi, Purang Fels, Sidney Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9783030322540;9783030322533

The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team. In this paper, an optimization framework is introduced to train deep models for completion (reconstruction) of the missing segments of the bone based on the remaining healthy structure. To leverage the contextual information of the surroundings of the dissected region, the voxel-weighted Dice loss is introduced. To address the non-deterministic nature of the shape completion problem, we leverage a weighted multi-target probabilistic solution which is an extension to the conditional variational autoencoder (CVAE). This approach considers multiple targets as acceptable reconstructions, each weighted according to their conformity with the original shape. We quantify the performance gain of the proposed method against similar algorithms, including CVAE, where we report statistically significant improvements in both deterministic and probabilistic paradigms. The probabilistic model is also evaluated on its ability to generate anatomically relevant variations for the missing bone. As a unique aspect of this work, the model is tested on real surgical cases where the clinical relevancy of its reconstructions and their compliance with surgeon's virtual plan are demonstrated as necessary steps towards clinical adoption.

关键词： conditional variational autoencoder 3D shape completion V-Net Mandible reconstruction

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Human Motion Trajectory Prediction Using the Social Force Model for Real-Time and Low Computational Cost Applications 6th

Human Motion Trajectory Prediction Using the Social Force Mo...

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6th Iberian Robotics Conference (Robot)

作者： Gil, Oscar Sanfeliu, Alberto CSIC UPC Inst Robot & Informat Ind Barcelona Spain

ISBN: (纸本)9783031586750;9783031586767

Human motion trajectory prediction is a very important functionality for human-robot collaboration, specifically in accompanying, guiding, or approaching tasks, but also in social robotics, self-driving vehicles, or security systems. In this paper, a novel trajectory prediction model, Social Force Generative Adversarial Network (SoFGAN), is proposed. SoFGAN uses a Generative Adversarial Network (GAN) and Social Force Model (SFM) to generate different plausible people trajectories reducing collisions in a scene. Furthermore, a conditional variational autoencoder (CVAE) module is added to emphasize the destination learning. We show that our method is more accurate in making predictions in UCY or BIWI datasets than most of the current state-of-the-art models and also reduces collisions in comparison to other approaches. Through real-life experiments, we demonstrate that the model can be used in real-time without GPU's to perform good quality predictions with a low computational cost.

关键词： Human Motion Prediction Social Force Model Generative Adversarial Network conditional variational autoencoder

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PoseCVAE: Anomalous Human Activity Detection 25

PoseCVAE: Anomalous Human Activity Detection

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25th International Conference on Pattern Recognition (ICPR)

作者： Jain, Yashswi Sharma, Ashvini Kumar Velmurugan, Rajbabu Banerjee, Biplab Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781728188089

Anomalous human activity detection is the task of identifying human activities that differ from the usual. Existing techniques, in general, try to deploy some samples from an open-set (anomalous activities can not be represented as a closed set) to define the discriminator. However, it is non-trivial to obtain novel activity instances. To this end, we propose PoseCVAE, a novel anomalous human activity detection strategy using the notion of generative modeling. We adopt a hybrid training strategy comprising of self-supervised and unsupervised learning. The self-supervised learning helps the encoder and decoder to learn better latent space representation of human pose trajectories. We train our framework to predict future pose trajectory given a normal track of past poses, i.e., the goal is to learn a conditional posterior distribution that represents normal training data. To achieve this we use a novel adaptation of a conditional variational autoencoder (CVAE) and refer it as PoseCVAE. Future pose prediction will be erroneous if the given poses are sampled from a distribution different from the learnt posterior, which is indeed the case with abnormal activities. To further separate the abnormal class, we imitate abnormal poses in the encoded space by sampling from a distinct mixture of Gaussians (MoG). We use a binary cross-entropy (BCE) loss as a novel addition to the standard CVAE loss function to achieve this. We test our framework on three publicly available datasets and achieve comparable performance to existing unsupervised methods that exploit pose information.

关键词： Stochastic Generative Models conditional variational autoencoder Pose Trajectory

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NVSRN: A Neural variational Scaling Reasoning Network for Initiative Response Generation 19

NVSRN: A Neural Variational Scaling Reasoning Network for In...

引用

19th IEEE International Conference on Data Mining (ICDM)

作者： Chang, Jinxin He, Ruifang Xu, Haiyang Han, Kun Wang, Longbiao Li, Xiangang Dang, Jianwu Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Didi Chuxing AI Labs Beijing Peoples R China Japan Adv Inst Sci & Technol Nomi Ishikawa Japan

ISBN: (纸本)9781728146034

Open-domain multi-turn dialogue systems are booming in human-machine interactions, which encourage to chat actively and freely in an intelligent natural way. Previous generative conversational models usually employ a single and deterministic encoder-decoder framework to model the semantic consistency between the context and corresponding response. However, they neglect the various dialog patterns (we denote the regularity of topic shifting as dialog pattern) in the conversations, leading to uninformative, non-initiative yet plausible responses. Although the existing variational methods have improved the response diversity to some extent by introducing a global variability into the generative process, they fail to simulate the transfer between topics with directional information due to the weak interpretability of the Gaussian-distributed latent variables. In this paper, we propose a novel Neural variational Scaling Reasoning Network (NVSRN) for initiative response generation. To this end, our approach has two core ingredients: neural dialog pattern reasoner (reasoner) and topic scaling mechanism. Specifically, inspired by the advantage of von Mises-Fisher (vMF) distribution modeling the directional data (e.g., the topic transfer state), we employ it as the latent space of the reasoner to explore the regularity of topic shifting, which is then used to reason the topic of response. Based on this, a topic scaling mechanism is designed to control the transfer degree of topic in the response generator. The experimental results on two large dialog datasets demonstrate that the proposed model outperforms state-of-the-art baselines. The human evaluation shows the proposed model can produce more informative and initiative responses actively.

关键词： dialogue generation topic shifting von MisesFisher distribution conditional variational autoencoder topic scaling mechanism

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A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security

A Unified Federated Learning Framework for Wireless Communic...

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39th IEEE International Conference on Computer Communications (IEEE INFOCOM)

作者： Wen, Hui Wu, Yue Yang, Chenming Duan, Hancong Yu, Shui Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Natl Key Lab Commun Chengdu Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186955

Training high-quality machine learning models on distributed systems is a critical issue to achieve edge intelligence in wireless communications. Conventional data-driven machine learning approaches are infeasible due to non-IID data caused by privacy issues and the limited communication resources in wireless networks. Besides, considering the complex user identities, the training process also faces the challenges of Byzantine devices, which can inject poisoning information into models. In this paper, we propose a two-step federated learning framework, robust federated augmentation and distillation (RFA-RFD), to enable privacy-preserving, communication-efficient, and Byzantine tolerant on-device machine learning in wireless communications. RFA is a method to tackle the problem of non-IID local data, which firstly trains local data generators on edge devices, then trains a global generator in the cloud server according to the IID dataset generated by the uploaded local generators, and finally, devices rectify non-IID dataset by downloading the global generator. After obtaining IID local data in edge devices, RFD is implemented to improve the performance of local models, in which devices only share the local information of models' outputs to reduce communication overhead. By employing a detection and discard mechanism in both RFA and RFD, our framework achieves robustness to the influence of Byzantine devices. Experiments show the effectiveness of RFA-RFD on preserving privacy, correcting non-IID data, reducing communication overhead, and resisting Byzantine devices, without much loss of accuracy compared with existing state-of-the-art methods.

关键词： Federated learning data augmentation distillation conditional variational autoencoder communication efficient privacy preserving Byzantine robustness

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Domain Reconstruction and Resampling for Robust Salient Object Detection 22

Domain Reconstruction and Resampling for Robust Salient Obje...

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30th ACM International Conference on Multimedia (MM)

作者： Yan, Senbo Peng, Liang Yu, Chuer Yang, Zheng Liu, Haifeng Cai, Deng Zhejiang Univ State Key Lab CAD & CG Hangzhou Zhejiang Peoples R China FABU Inc Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450392037

Salient Object Detection (SOD) aims at detecting the salient objects covering the whole natural scene. However, one of the main problems in SOD is data bias. Natural scenes vary greatly, while each image in the SOD dataset contains a specific scene. It means that each image is just a sampling point in a specific scene, which is not representative and causes serious sampling bias. Building larger datasets is one solution but costly to address the sampling bias. Our method regards the data distribution of natural scenes as a Gaussian Mixture Distribution, and each scene follows a sub-Gaussian distribution. Our main idea is to reconstruct the data distribution of each scene from the sampling images and then resample from the distribution domain. We represent a scene by a distribution instead of a fixed sampling image to reserve the sampling uncertainty in SOD. Specifically, we employ a Style conditional variational autoencoder (Style-CVAE) to reconstruct the data distribution from image styles and a Gaussian Randomize Attribute Filter (GRAF) to reconstruct data distribution from image attributes (such as lightness, saturation, hue, etc.). We resample the reconstructed data distribution according to the Gaussian probability density function and train the SOD model. Experimental results prove that our method outperforms 16 state-of-the-art methods on five benchmarks.

关键词： Salient Object Detection Domain Reconstruction conditional variational autoencoder

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Inference of electromagnetic system behavior in the presence of variability

Inference of electromagnetic system behavior in the presence...

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作者： Ma, Xiao University of Illinois – Urbana-Champaign

学位级别：博士

This thesis contributes to and advances the state-of-the-art of the analysis of stochastic electromagnetic-circuit systems by applying novel statistical inference and generative modeling techniques to the extraction of statistical models of system responses. Machine learning based and physics based techniques are introduced to address the limitations of existing methods by incorporating dimensionality reduction into the model training procedure, enabling the enforcement of certain physical constraints, making the modeling procedure more flexible by accepting additional data to capture dependency, and taking advantage of universal statistics to reduce the sample requirement. Specifically, for the first time, a variational autoencoder based method is used for the generative modeling of high-dimensional S-parameters data. The generation accuracy is shown to be superior to that of existing methods. The passive variational autoencoder, a variational autoencoder with modified decoder architecture, is introduced to enforce physical constraints like passivity. The generated S-parameters are shown to be physically consistent. The generation accuracy and efficiency of the proposed method are assessed through comparison to those of existing methods. When a system is decomposed into multiple subsystems, the dependency between responses of subsystems needs to be captured but is often neglected. A conditional variational autoencoder is introduced for the capturing of such dependency. Additional control variables are used to account for the randomness external to a subsystem. The conditional variational autoencoder based generative model demonstrates an advantage in terms of generation accuracy as compared to its standard version, which ignores the dependency between subsystems. The random coupling model is a physics based generative model applicable to electromagnetic enclosures under wave chaos condition. The low-frequency limit of the random coupling model based generative mode

关键词： Stochastic electromagnetic-circuit analysis Statistical inference Generative modeling S-parameters variational autoencoder Physical constraints Passivity Dependency between subsystems conditional variational autoencoder Universal statistics Random coupling model Wave chaos

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