Background and objective : Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease ...
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Background and objective : Diabetes is a chronic pathology which is affecting more and more people over the years. It gives rise to a large number of deaths each year. Furthermore, many people living with the disease do not realize the seriousness of their health status early enough. Late diagnosis brings about numerous health problems and a large number of deaths each year so the development of methods for the early diagnosis of this pathology is essential.& nbsp;& nbsp;Methods: In this paper, a pipeline based on deep learning techniques is proposed to predict diabetic peo-ple. It includes data augmentation using a variational autoencoder (VAE), feature augmentation using an sparse autoencoder (SAE) and a convolutional neural network for classification. Pima Indians Diabetes Database, which takes into account information on the patients such as the number of pregnancies, glu-cose or insulin level, blood pressure or age, has been evaluated.& nbsp;Results: A 92 . 31% of accuracy was obtained when CNN classifier is trained jointly the SAE for featuring augmentation over a well balanced dataset. This means an increment of 3.17% of accuracy with respect the state-of-the-art.& nbsp;Conclusions : Using a full deep learning pipeline for data preprocessing and classification has demonstrate to be very promising in the diabetes detection field outperforming the state-of-the-art proposals.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.
variational autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguo...
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variational autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguous, and GANs are prone to the mode collapse, which lead the poor generalization abilities ultimately. Moreover, most of these models only consider the extraction of spectral or spatial features. They fail to combine the two branches interactively and ignore the correlation between them. Consequently, the variational generative adversarial network with crossed spatial and spectral interactions (CSSVGAN) was proposed in this paper, which includes a dual-branch variational Encoder to map spectral and spatial information to different latent spaces, a crossed interactive Generator to improve the quality of generated virtual samples, and a Discriminator stuck with a classifier to enhance the classification performance. Combining these three subnetworks, the proposed CSSVGAN achieves excellent classification by ensuring the diversity and interacting spectral and spatial features in a crossed manner. The superior experimental results on three datasets verify the effectiveness of this method.
This paper applies a generative deep learning model, namely a variational autoencoder, on probabilistic optimal power flows. The model utilizes Gaussian approximations in order to adequately represent the distribution...
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This paper applies a generative deep learning model, namely a variational autoencoder, on probabilistic optimal power flows. The model utilizes Gaussian approximations in order to adequately represent the distributions of the results of a system under uncertainty. These approximations are realized by applying several techniques from Bayesian deep learning, among them most notably Stochastic variational Inference. Using the reparameterization trick and batch sampling, the proposed model allows for the training a probabilistic optimal power flow similar to a possibilistic process. The results are shown by application of a reformulation of the Kullback-Leibler divergence, a distance measure of distributions. Not only is the resulting model simple in its appearance, it also shows to perform well and accurate. Furthermore, the paper also explores potential pathways for future research and gives insights for practitioners using such or similar generative models.
Face aging and rejuvenating aim to generate an individual face with aging and rejuvenating effect while retaining identity information. We can analyze a given face image to estimate a past look or predict a future loo...
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Face aging and rejuvenating aim to generate an individual face with aging and rejuvenating effect while retaining identity information. We can analyze a given face image to estimate a past look or predict a future look of the person. The research on face aging and rejuvenating has important application value in the fields of cross-age recognition,1 public security, and entertainment, for example, changing the appearance of actors at different ages in a movie or finding missing persons in forensic applications. Although this area has attracted much attention of the researchers, there are still many challenges, especially in lack of accurate and sufficient dataset, low aging effect, and bad identity preservation. Previous face aging and rejuvenating methods are split into two main categories: physical model-based methods2,3 and prototype-based methods.4-6 The physical model-based methods describe the alteration in muscle, wrinkle, skin, etc., which can get a good result but suffer from complex modeling. The prototype-based methods try to learn the transformation between different age groups. Due to the development of generative adversarial network (GAN),7 state-of-theart methods8-10 that use the technology of deep learning show impressive success in this field. Face aging and rejuvenating work effectively in public security criminal investigation, cross-age recognition, and entertainment. However, three main problems still exist: the lack of accurate and sufficient dataset, low aging effect, and poor preservation of personal information. We propose a semi-supervised face aging and rejuvenating method for face aging and rejuvenating. In particular, a conditional encoder is utilized to map an input face into a latent vector, which is used by the generator network with age conditions to produce a new face. The latent vector preserves identity information, whereas the age label controls face aging or rejuvenating. To make generated features closer to prior features, the d
Unsupervised abnormality detection is an appealing approach to identify patterns that are not present in training data without specific annotations for such patterns. In the medical imaging field, methods taking this ...
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Unsupervised abnormality detection is an appealing approach to identify patterns that are not present in training data without specific annotations for such patterns. In the medical imaging field, methods taking this approach have been proposed to detect lesions. The appeal of this approach stems from the fact that it does not require lesion-specific supervision and can potentially generalize to any sort of abnormal patterns. The principle is to train a generative model on images from healthy individuals to estimate the distribution of images of the normal anatomy, i.e., a normative distribution , and detect lesions as out-of distribution regions. Restoration-based techniques that modify a given image by taking gradient ascent steps with respect to a posterior distribution composed of a normative distribution and a likelihood term recently yielded state-of-the-art results. However, these methods do not explicitly model ascent directions with respect to the normative distribution, i.e. normative ascent direction, which is essential for successful restoration. In this work, we introduce a novel approach for unsupervised lesion detection by modeling normative ascent directions. We present different modelling options based on the defined ascent directions with local Gaussians. We further extend the proposed method to efficiently utilize 3D information, which has not been explored in most existing works. We experimentally show that the proposed method provides higher accuracy in detection and produces more realistic restored images. The performance of the proposed method is evaluated against baselines on publicly available BRATS and ATLAS stroke lesion datasets;the detection accuracy of the proposed method surpasses the current stateof-the-art results. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )
variational autoencoder (VAE) is a popular latent variable model for data generation. However, in natural language applications, VAE suffers from the posterior collapse in optimization procedure where the model poster...
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ISBN:
(纸本)9781728169262
variational autoencoder (VAE) is a popular latent variable model for data generation. However, in natural language applications, VAE suffers from the posterior collapse in optimization procedure where the model posterior likely collapses to a standard Gaussian prior which disregards latent semantics from sequence data. The recurrent decoder accordingly generates duplicate or noninformative sequence data. To tackle this issue, this paper adopts the Gaussian mixture prior for latent variable, and simultaneously fulfills the amortized regularization in encoder and skip connection in decoder. The noise robust prior, learned from the amortized encoder, becomes semantically meaningful. The prediction of sequence samples, due to skip connection, becomes contextually precise at each time. The amortized mixture prior (AMP) is then formulated in construction of variational recurrent autoencoder (VRAE) for sequence generation. Experiments on different tasks show that AMP-VRAE can avoid the posterior collapse, learn the meaningful latent features and improve the inference and generation for semantic representation.
Generalized zero-shot learning (GZSL) for image classification is a challenging task since not only training examples from novel classes are absent, but also classification performance is judged on both seen and unsee...
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ISBN:
(纸本)9781728113319
Generalized zero-shot learning (GZSL) for image classification is a challenging task since not only training examples from novel classes are absent, but also classification performance is judged on both seen and unseen classes. This setting is vital in realistic scenarios where the vast labeled data are not easily available. Some existing methods for GZSL utilize latent features learned through variational autoencoder (VAE) for recognizing novel classes, while few have solved the problem that image features have large intra-class variance affecting the quality of latent features. Hence we propose to match the soul samples to shorten the variance regularized by the pre-trained classifiers, which enables the VAE to generate much more discriminative latent features to train the softmax classifier. We evaluate our method on four benchmark datasets, i.e. CUB, SUN, AWA1, AWA2, and experimental results demonstrate that our model achieves the new state-of-the-art in generalized zero-shot and few-shot learning settings.
Sufficient data are necessary for valid process monitoring results. However, modern industrial processes sometimes switch to new modes to meet the changes in market demand. The available data in such a new mode are in...
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Sufficient data are necessary for valid process monitoring results. However, modern industrial processes sometimes switch to new modes to meet the changes in market demand. The available data in such a new mode are initially quite scarce and it brings huge obstacles to data-based model construction. In this paper, a novel data synthesis method based on variational autoencoders is proposed to generate synthetic data for the data-scarce region. The proposed method utilizes not only the original data in the data-scarce region but also the data in other data-intensive regions, which share some common information with the scarce data. To avoid model biases caused by the data imbalance between these regions, a model correction mechanism is also developed. Once the ultimate synthetic data of the data-scarce region are acquired, they are combined with the original data to establish a local monitoring model. Finally, the effectiveness of the proposed method is demonstrated through a real ammonia synthesis process.
Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDSs), aims at converting a meaning representation into natural language utterances. Recent deep learning-based generators have shown...
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Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDSs), aims at converting a meaning representation into natural language utterances. Recent deep learning-based generators have shown improving results irrespective of providing sufficient annotated data. Nevertheless, how to build a generator that can effectively utilize as much of knowledge from a low-resource setting data is a crucial issue for NLG in SDSs. This paper presents a variational-based NLG framework to tackle the NLG problem of having limited annotated data in two scenarios, domain adaptation and low-resource in-domain training data. Based on this framework, we propose a novel adversarial domain adaptation NLG taclking the former issue, while the latter issue is also handled by a second proposed dual variational model. We extensively conducted the experiments on four different domains in a variety of training scenarios, in which the experimental results show that the proposed methods not only outperform previous methods when having sufficient training dataset but also show its ability to work acceptably well when there is a small amount of in-domain data or adapt quickly to a new domain with only a low-resource target domain data. (C) 2020 Published by Elsevier Ltd.
This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involve...
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This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involves a specially-tailored form of conditioning that allows us to simplify the VAE decoder structure while simultaneously introducing robustness to outliers. In a related vein, a second, complementary alteration is proposed to further build invariance to contaminated or dirty samples via a data augmentation process that amounts to recycling. In brief, to the extent that the VAE is legitimately a representative generative model, then each output from the decoder should closely resemble an authentic sample, which can then be resubmitted as a novel input ad infinitum. Moreover, this can be accomplished via special recurrent connections without the need for additional parameters to be trained. We evaluate these proposals on multiple practical outlier-removal and generative modeling tasks involving nonlinear low-dimensional manifolds, demonstrating considerable improvements over existing algorithms.
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