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检索条件"主题词=Variational autoencoder"

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Diversity Regularized autoencoders for Text Generation 20

Diversity Regularized Autoencoders for Text Generation

引用

35th Annual ACM Symposium on Applied Computing (SAC)

作者： Ko, Hyeseon Lee, Junhyuk Kim, Jinhong Lee, Jongwuk Shim, Hyunjung Sungkyunkwan Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9781450368667

In this paper, we propose a simple yet powerful text generation model, called diversity regularized autoencoders (DRAE). The key novelty of the proposed model lies in its ability to handle various sentence modifications such as insertions, deletions, substitutions, and maskings, and to take them as input. Because the noise injection strategy enables an encoder to make the latent distribution smooth and continuous, the proposed model can generate more diverse and coherent sentences. Also, we adopt the Wasserstein generative adversarial networks with a gradient penalty to achieve stable adversarial training of the prior distribution. We evaluate the proposed model using quantitative, qualitative, and human evaluations on two public datasets. Experimental results demonstrate that our model using a noise-injection strategy produces more natural and diverse sentences than several baseline models. Furthermore, it is found that our model shows the synergistic effect of grammar correction and paraphrase generation in an unsupervised way.

关键词： variational autoencoder Adversarial training Data augmentation

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AN autoencoder-BASED APPROACH FOR RECOGNIZING NULL CLASS IN ACTIVITIES OF DAILY LIVING IN-THE-WILD VIA WEARABLE MOTION SENSORS 44

AN AUTOENCODER-BASED APPROACH FOR RECOGNIZING NULL CLASS IN ...

引用

44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Akbari, Ali Jafari, Roozbeh Texas A&M Univ Dept Biomed Engn College Stn TX 77843 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781479981311

Recognizing activities of daily living (ADL) in-the-wild, while users follow their daily routine, is challenging due to the presence of various activities that do not belong to the set of desired activities in which the system is interested (i.e., NULL class). In this paper, we propose a framework for ADL recognition via wearable motion sensors with the ability to detect NULL class. Existing ADL recognition systems either ignore the NULL class or use some training data to train a model for recognizing it. However, our framework uses only samples of the desired activities in the training phase and learns to detect the NULL samples based on a modified variational autoencoder model that outputs reconstruction probability. Experimental results show that in detecting six ADL with accelerometer data, our system achieves 14% higher F1-score compared to the models that use training samples of NULL activities.

关键词： Wearable motion sensor ADL recognition variational autoencoder NULL class detection

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Perioperative Predictions with Interpretable Latent Representation 22

Perioperative Predictions with Interpretable Latent Represen...

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

作者： Xue, Bing Jiao, York Kannampallil, Thomas Fritz, Bradley King, Christopher Abraham, Joanna Avidan, Michael Lu, Chenyang Washington Univ McKelvey Sch Engn St Louis MO 63110 USA Washington Univ Sch Med St Louis MO 63110 USA

ISBN: (纸本)9781450393850

Given the risks and cost of hospitalization, there has been significant interest in exploiting machine learning models to improve perioperative care. However, due to the high dimensionality and noisiness of perioperative data, it remains a challenge to develop accurate and robust encoding for surgical predictions. Furthermore, it is important for the encoding to be interpretable by perioperative care practitioners to facilitate their decision making process. We propose clinical variational autoencoder (cVAE), a deep latent variable model that addresses the challenges of surgical applications through two salient features. (1) To overcome performance limitations of traditional VAE, it is prediction-guided with explicit expression of predicted outcome in the latent representation. (2) It disentangles the latent space so that it can be interpreted in a clinically meaningful fashion. We apply cVAE to two real-world perioperative datasets to evaluate its efficacy and performance in predicting outcomes that are important to perioperative care, including postoperative complication and surgery duration. To demonstrate the generality and facilitate reproducibility, we also apply cVAE to the open MIMIC-III dataset for predicting ICU duration and mortality. Our results show that the latent representation provided by cVAE leads to superior performance in classification, regression and multi-task predictions. The two features of cVAE are mutually beneficial and eliminate the need of a predictor. We further demonstrate the interpretability of the disentangled representation and its capability to capture intrinsic characteristics of hospitalized patients. While this work is motivated by and evaluated in the context of clinical applications, the proposed approach may be generalized for other fields using high-dimensional and noisy data and valuing interpretable representations.

关键词： clinical prediction healthcare variational autoencoder

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Manifold-based Test Generation for Image Classifiers 2

Manifold-based Test Generation for Image Classifiers

引用

IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Byun, Taejoon Vijayakumar, Abhishek Rayadurgam, Sanjai Cofer, Darren Univ Minnesota Comp Sci & Eng Minneapolis MN 55455 USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Collins Aerosp Adv Technol Ctr Bloomington MN USA

ISBN: (数字)9781728169842

ISBN: (纸本)9781728169842

Neural networks used for image classification tasks in critical applications must be tested with sufficient realistic data to assure their correctness. This raises two challenges: first, an adequate subset of the data points must be carefully chosen to inspire confidence, and second, the implicit requirements must be meaningfully extrapolated to data points beyond those in the explicit training set. This paper proposes a novel framework to address these challenges. Our approach is based on the premise that patterns in a large input data space can be effectively captured in a smaller manifold space, from which similar yet novel test cases-both the input and the label-can be sampled and generated. A variant of Conditional variational autoencoder (CVAE) is used for capturing this manifold with a generative function, and a search technique is applied on this manifold space to efficiently find fault-revealing inputs. Experiments show that this approach enables generation of thousands of realistic yet fault-revealing test cases efficiently even for well-trained models.

关键词： machine learning testing test generation neural networks variational autoencoder

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JOINT DATA-DRIVEN ANALYSIS OF VISUAL-ODOMETRIC ANOMALY SIGNALS IN GENERATIVE AI-BASED AGENTS 49

JOINT DATA-DRIVEN ANALYSIS OF VISUAL-ODOMETRIC ANOMALY SIGNA...

引用

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

作者： Slavic, Giulia Bracco, Mattia Marcenaro, Lucio Gomez, David Martin Regazzoni, Carlo Zontone, Pamela Univ Genoa Genoa Italy Univ Carlos III Madrid Getafe Spain

ISBN: (纸本)9798350374520;9798350374513

This paper presents a data-driven model that, by exploring the correlation between the data coming from two sensors (GPS and camera), allows us to explain the odometry anomalies by analyzing video data. Our approach uses a Markov Jump Particle Filter (MJPF) to process the odometry data of a vehicle, extracting its features and identifying instant anomalies. Simultaneously, the object causing the anomaly is detected and tracked in the video data. After that, the correlation between the odometric trajectories and the object trajectories is determined in both normal and abnormal cases. Another correlation coefficient is then employed to calculate the distance between the computed correlations. The proposed method is evaluated using multi-modal data collected from a vehicle operating in a closed environment, where pedestrians represent anomalies. We show that our system is able to distinguish which video anomaly better explains the odometry anomaly.

关键词： variational autoencoder anomaly detection particle filtering Kalman filtering explainability

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Image-to-Video Translation Using a VAE-GAN with Refinement Network 18th

Image-to-Video Translation Using a VAE-GAN with Refinement N...

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18th International Conference on Intelligent Computing (ICIC)

作者： Wang, Shengli Xieshi, Mulin Zhou, Zhangpeng Zhang, Xiang Liu, Xujie Tang, Zeyi Xiahou, Jianbing Lin, Pingyuan Xu, Xuexin Dai, Yuxing Maintenance Co State Grid Power Co Gansu Prov Lanzhou 730000 Gansu Peoples R China State Grid Infotelecom Great Power Sci & Technol Fuzhou 350000 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

ISBN: (纸本)9783031138706;9783031138690

With the development of deep learning technology, various techniques for image processing have emerged in the field of computer vision in recent years, and have excellent performance in a variety of application scenarios. In contrast to the prediction task of predicting video with multiple consecutive frames before and after the input to predict the missing images in the middle, the task of image-to-video generation proposed in this paper does not require multiple consecutive frames, but rather the directional content generation of images by inputting the first frame image with the embedding vector of motion features, and to address some of the existing problems, this paper innovates the network architecture to solve the generated video problems such as incoherence, frame loss and blurring. For multiple image-to-video translation tasks, we propose a VAE-RGAN network with a further refinement network. We add a refinement network and use new identity matching loss and connected feature matching loss to eliminate VAE and GAN's respective shortcomings and enhance the visual quality of the generated videos. Weizmann datasets have been the subject of a wide range of qualitative and quantitative experiments. We draw the following conclusions from this empirical study: (1) Compared with state-of-the-art approaches, our approach (VAE-RGAN) exhibits significant improvements in generative capability;(2) Experiments shows that our designed VAE-RGAN structure achieves better results and the refinement network significantly improves the problems of a blur.

关键词： Video generation variational autoencoder Generative Adversarial Network Refinement network

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Motor Imagery EEG Signal Recognition Based on ACVAE and CNN-LSTM

Motor Imagery EEG Signal Recognition Based on ACVAE and CNN-...

引用

International Conference on Electronic Engineering and Information Systems (EEISS)

作者： Hu, Cunlin Ye, Ye Li, Jian Wang, Hongliang Zhou, Tao Xie, Nenggang Anhui Univ Technol Coll Mech Engn Maanshan Peoples R China Maanshan Peoples Hosp Maanshan Peoples R China Anhui Univ Technol Coll Management Sci & Engn Maanshan Peoples R China

ISBN: (纸本)9798350351040;9798350351033

Hybrid neural networks are able to capture the time-dependency of electroencephalography (EEG) signals, and can therefore effectively perform pattern recognition of motor imagery EEG signals. However, a sufficiently large amount of training data is required to achieve optimal results, so it is necessary to use data augmentation methods to increase the amount of data. Therefore, we propose a data augmentation method based on the Attention Convolutional Variation autoencoder (ACVAE) and design a Convolutional :Neural Network-Long Short -Term Memory (CNN-LSTN1) network for pattern recognition. The BCI Competition IV dataset 2a is used for experimental validation. The results show that the ACVAE method produces higher quality data, with the highest recognition accuracy of 97.16% for a single subject in the four-classification task. Compared to other methods, we proposed method shows excellent performance.

关键词： Convolutional,NTeural Network Long Short-Term Memory EEG recognition variational autoencoder

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3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks 21st

3D Fetal Skull Reconstruction from 2DUS via Deep Conditional...

引用

21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Cerrolaza, Juan J. Li, Yuanwei Biffi, Carlo Gomez, Alberto Sinclair, Matthew Matthew, Jacqueline Knight, Caronline Kainz, Bernhard Rueckert, Daniel Imperial Coll London Biomed Image Anal Grp London England Kings Coll London Div Imaging Sci & Biomed Engn London England

ISBN: (纸本)9783030009281;9783030009274

2D ultrasound (US) is the primary imaging modality in antenatal healthcare. Despite the limitations of traditional 2D biometrics to characterize the true 3D anatomy of the fetus, the adoption of 3DUS is still very limited. This is particularly significant in developing countries and remote areas, due to the lack of experienced sonographers and the limited access to 3D technology. In this paper, we present a new deep conditional generative network for the 3D reconstruction of the fetal skull from 2DUS standard planes of the head routinely acquired during the fetal screening process. Based on the generative properties of conditional variational autoencoders (CVAE), our reconstruction architecture (REC-CVAE) directly integrates the three US standard planes as conditional variables to generate a unified latent space of the skull. Additionally, we propose HiREC-CVAE, a hierarchical generative network based on the different clinical relevance of each predictive view. The hierarchical structure of HiREC-CVAE allows the network to learn a sequence of nested latent spaces, providing superior predictive capabilities even in the absence of some of the 2DUS scans. The performance of the proposed architectures was evaluated on a dataset of 72 cases, showing accurate reconstruction capabilities from standard non-registered 2DUS images.

关键词： Generative model variational autoencoder Fetal ultrasound

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Detecting Anomalies with LatentOut: Novel Scores, Architectures, and Settings 26th

Detecting Anomalies with LatentOut: Novel Scores, Architectu...

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26th International Symposium on Methodologies for Intelligent Systems (ISMIS)

作者： Angiulli, Fabrizio Fassetti, Fabio Ferragina, Luca Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783031165641;9783031165634

LatentOut is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (variational) autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we extend the research on the LatentOut methodology in three directions: first, we provide a novel score performing a different kind of density estimation at a reduced computational cost;second, we experiment the combination of LatentOut with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection;third, we investigate performances of LatentOut acting as a one-class classifier. The experiments show that all the variants of LatentOut here introduced improve performances of the baseline methods to which they are applied, both in the unsupervised and in the semi-supervised settings.

关键词： Anomaly detection variational autoencoder Generative adversarial network Density estimation

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Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation 23

Assisting Clinical Decisions for Scarcely Available Treatmen...

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29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Xue, Bing Said, Ahmed Sameh Xu, Ziqi Liu, Hanyang Shah, Neel Yang, Hanqing Payne, Philip Lu, Chenyang Washington Univ McKelvey Sch Engn St Louis MO 63110 USA Washington Univ Sch Med St Louis MO USA

ISBN: (纸本)9798400701030

Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and technically complex treatment option. To support clinical decisions, it is a critical need to predict the treatment need and the potential treatment and no-treatment responses. Targeting this clinical challenge, we propose Treatment variational autoencoder (TVAE), a novel approach for individualized treatment analysis. TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases. TVAE conceptualizes the treatment decision as a multi-scale problem. We model a patient's potential treatment assignment and the factual and counterfactual outcomes as part of their intrinsic characteristics that can be represented by a deep latent variable model. The factual and counterfactual prediction errors are alleviated via a reconstruction regularization scheme together with semi-supervision, and the selection bias and the scarcity of treatment cases are mitigated by the disentangled and distribution-matched latent space and the label-balancing generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an international dataset collected from 1651 hospitals across 63 countries, and a institutional dataset collected from 15 hospitals. The results show that TVAE outperforms state-of-the-art treatment effect models in predicting both the propensity scores and factual outcomes on heterogeneous COVID-19 datasets. Additional experiments also show TVAE outperforms the best existing models in individual treatment effect estimation on the synthesized IHDP benchmark dataset.

关键词： Machine Learning for Healthcare Causal Inference Representation Learning Semi-supervised Learning Treatment Effect Estimation COVID Analysis Generative AI Deep Latent Variable Models variational autoencoder

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