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检索条件"主题词=Autoencoder"

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Multi-Document Extractive Text Summarization via Deep Learning Approach 5

Multi-Document Extractive Text Summarization via Deep Learni...

引用

IEEE 5th Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Rezaei, Afsaneh Dami, Sina Daneshjoo, Parisa Islamic Azad Univ West Tehran Branch Dept Comp Engn Tehran Iran

ISBN: (纸本)9781728108728

Today, given the huge amount of information, summarization has become one of the most applicable topics in data mining that can help users gain access to useful data over a short period of time. In this study, two multi-document extractive text Summarization systems are introduced. The major objective of this research is to use autoencoder neural network and deep belief network separately for scoring sentences in a document to compare their performances. Deep neural networks can improve the results by generating new features. The abovementioned systems were tested on DUC 2007 dataset and evaluated using ROUGE-1 and ROUGE-2 criteria. The results show a better performance of autoencoder network versus deep belief network. It is also possible to compare these values with results of other systems to realize the effectiveness of the proposed methods.

关键词： Multi-Document summarization Deep learning Extractive summarization autoencoder DBN

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Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning 41

Deep Learning Based Dosimetry Evaluation at Organs-at-Risk i...

引用

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jiang, Dashan Li, Teng Mao, Ronghu Du, Chi Liu, Jianfei Anhui Univ Elect Engn & Automat Hefei Peoples R China Zhengzhou Univ Henan Canc Hosp Dept Radiat Oncol Affiliated Canc Hosp Zhengzhou Henan Peoples R China Second Peoples Hosp Neijiang Canc Ctr Neijiang 641000 Sichuan Peoples R China

ISBN: (纸本)9781538613115

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) and organs at risk (OARs). DVH is highly correlated with the geometrical relationship between PTV and OARs, which motivates us to explore deep learning techniques to model such correlation and predict DVHs of different OARs. Distance to target histogram (DTH) is chosen to measure the geometrical relationship between PTV and OARs. DTH and DVH features are then undergone dimension reduction by autoencoder. The reduced feature vectors are finally imported into deep belief network to model the correlation between DTH and DVH. This correlation can be used to predict DVH of the corresponding OAR for new patients. Validation results revealed that the relative dose difference of the predicted and clinical DVHs on four different OARs were less than 3 %. These promising results suggested that the predicted DVH could provide near-optimal parameters to significantly reduce the planning time.

关键词： Esophagus Dose volume histogram autoencoder Deep belief network

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Deep Learning-based Method for Classifying and Localizing Potato Blemishes 8th

Deep Learning-based Method for Classifying and Localizing Po...

引用

8th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Marino, Sofia Beauseroy, Pierre Smolarz, Andre Univ Technol Troyes Inst Charles Delaunay M2S FRE 2019 Troyes France

ISBN: (纸本)9789897583513

In this paper we address the problem of potato blemish classification and localization. A large database with multiple varieties was created containing 6 classes, i.e., healthy, damaged, greening, black dot, common scab and black scurf. A Convolutional Neural Network was trained to classify face potato images and was also used as a filter to select faces where more analysis was required. Then, a combination of autoencoder and SVMs was applied on the selected images to detect damaged and greening defects in a patch-wise manner. The localization results were used to classify the potato according to the severity of the blemish. A final global evaluation of the potato was done where four face images per potato were considered to characterize the entire tuber. Experimental results show a face-wise average precision of 95% and average recall of 93%. For damaged and greening patch-wise localization, we achieve a False Positive Rate of 4.2% and 5.5% and a False Negative Rate of 14.2% and 28.1% respectively. Concerning the final potato-wise classification, we achieved in a test dataset an average precision of 92% and average recall of 91%.

关键词： Deep Learning Potato Blemishes Classification Localization autoencoder SVM

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Homogenization of Breast MRI across Imaging Centers and Feature Analysis using Unsupervised Deep Embedding

Homogenization of Breast MRI across Imaging Centers and Feat...

引用

Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Samala, Ravi K. Chan, Heang-Ping Hadjiiski, Lubomir Paramagul, Chintana Helvie, Mark A. Neal, Colleen H. Univ Michigan Dept Radiol Ann Arbor MI 48109 USA

ISBN: (数字)9781510625488

ISBN: (纸本)9781510625488

We propose an intensity-based technique to homogenize dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data acquired at six institutions. A total of 234 T1-weighted MRI volumes acquired at the peak kinetic curve were obtained for study of the homogenization and unsupervised deep-learning feature extraction techniques. The homogenization uses reference regions of adipose breast tissue since they are less susceptible to variations due to cancer and contrast medium. For the homogenization, the moments of the distribution of reference pixel intensities across the cases were matched and the remaining intensity distributions were matched accordingly. A deep stacked autoencoder with six convolutional layers was trained to reconstruct a 128x128 MRI slice and to extract a latent space of 1024 dimensions. We used the latent space from the stacked autoencoder to extract deep embedding features that represented the global and local structures of the imaging data. An analysis using spectral embedding of the latent space shows that, before homogenization the dominating factor was the dependency on the imaging center;after homogenization the histograms of the cases between different centers were matched and the center dependency was reduced. The results of feature analysis indicate that the proposed homogenization approach may lessen the effects of different imaging protocols and scanners in MRI, which may then allow more consistent quantitative analysis of radiomic information across patients and improve the generalizability of machine learning methods across different clinical sites. Further study is underway to evaluate the performance of machine learning models with and without image homogenization.

关键词： breast cancer magnetic resonance imaging autoencoder clustering spectral embedding

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A Closer Look at Disentangling in β-VAE 53

A Closer Look at Disentangling in β-VAE

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53rd Asilomar Conference on Signals, Systems, and Computers (ACSSC)

作者： Sikka, Harshvardhan Zhong, Weishun Yin, Jun Pehlevan, Cengiz Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA MIT Dept Phys Cambridge MA 02139 USA Harvard Univ Ctr Brain Sci Cambridge MA 02138 USA Harvard Univ Dept Phys Cambridge MA 02138 USA

ISBN: (纸本)9781728143002

In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating factors are also statistically independent, disentangled representations can be formed by Bayesian inference of latent variables. We examine a generalization of the Variational autoencoder (VAE), beta-VAE, for learning such representations using variational inference. beta-VAE enforces conditional independence of its bottleneck neurons controlled by its hyperparameter beta. This condition is in general not compatible with the statistical independence of latents. By providing analytical and numerical arguments, we show that this incompatibility leads to a non-monotonic inference performance in beta-VAE with a finite optimal beta.

关键词： autoencoder Bayesian Inference

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EE-AE: AN EXCLUSIVITY ENHANCED UNSUPERVISED FEATURE LEARNING APPROACH 44

EE-AE: AN EXCLUSIVITY ENHANCED UNSUPERVISED FEATURE LEARNING...

引用

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

作者： Guo, Jingcai Guo, Song Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781479981311

Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ignores the inherent relation of data, i.e., statistical and geometrical dependence, and easily causes overfitting. In order to deal with this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature learning approach to improve the conventional AE. To the best of our knowledge, our research is the first to utilize such exclusivity concept to cooperate with feature extraction within AE. Moreover, in this paper we also make some improvements to the stacked AE structure especially for the connection of different layers from decoders, this could be regarded as a weight initialization trial. The experimental results show that our proposed approach can achieve remarkable performance compared with other related methods.

关键词： Unsupervised learning Exclusivity Feature learning autoencoder

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Rethinking Planar Homography Estimation Using Perspective Fields 14th

Rethinking Planar Homography Estimation Using Perspective Fi...

引用

14th Asian Conference on Computer Vision (ACCV)

作者： Zeng, Rui Denman, Simon Sridharan, Sridha Fookes, Clinton Queensland Univ Technol Brisbane Qld Australia

ISBN: (纸本)9783030208769;9783030208752

Planar homography estimation refers to the problem of computing a bijective linear mapping of pixels between two images. While this problem has been studied with convolutional neural networks (CNNs), existing methods simply regress the location of the four corners using a dense layer preceded by a fully-connected layer. This vector representation damages the spatial structure of the corners since they have a clear spatial order. Moreover, four points are the minimum required to compute the homography, and so such an approach is susceptible to perturbation. In this paper, we propose a conceptually simple, reliable, and general framework for homography estimation. In contrast to previous works, we formulate this problem as a perspective field (PF), which models the essence of the homography - pixel-to-pixel bijection. The PF is naturally learned by the proposed fully convolutional residual network, PFNet, to keep the spatial order of each pixel. Moreover, since every pixels' displacement can be obtained from the PF, it enables robust homography estimation by utilizing dense correspondences. Our experiments demonstrate the proposed method outperforms traditional correspondence-based approaches and state-of-the-art CNN approaches in terms of accuracy while also having a smaller network size. In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture.

关键词： Homography autoencoder Perspective field PFNet

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Combining feature selection, feature learning and ensemble learning for software fault prediction 11

Combining feature selection, feature learning and ensemble l...

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11th International Conference on Knowledge and Systems Engineering (KSE)

作者： Hung Duy Tran Le Thi My Hanh Nguyen Thanh Binh Univ Danang Dept Informat Technol Univ Sci & Technol Danang Vietnam

ISBN: (纸本)9781728130033

This paper studies a combination of feature selection and ensemble learning to address the feature redundancy and class imbalance problems in software fault prediction. Also, a deep learning model is used to generate deep representation from defect data to improve the performance of fault prediction models. The proposed method, GFsSDAEsTSE, is evaluated on 12 NASA datasets, and the results show that GFsSDAEsTSE outperforms state-of-the-art methods in both small and large datasets.

关键词： software fault prediction feature generation autoencoder ensemble learning feature selection

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A Comparison of CasPer Against Other ML Techniques for Stress Recognition 26th

A Comparison of CasPer Against Other ML Techniques for Stres...

引用

26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Sekoranja, Jack Michael Harding Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9783030368081;9783030368074

When developing multi-layer neural networks (MLNNs), determining an appropriate size can be computationally intensive. Cascade Correlation algorithms such as CasPer attempt to address this, however, associated research often uses artificially constructed data. Additionally, few papers compare the effectiveness with standard MLNNs. This paper takes the ANUstressDB database and applies a genetic algorithm autoencoder to reduce the number of features. The efficiency and accuracy of CasPer on this dataset is then compared to CasCor, MLNN, KNN, and SVM. Results indicate the training time for CasPer was much lower than the MLNNs at a small cost to prediction accuracy. CasPer also had similar training efficiency to simple algorithms such as SVM, yet had a higher predictive ability. This indicates CasPer would be a good choice for difficult problems that require small training times. Furthermore, the cascading feature of the network makes it better at fitting to unknown problems, while remaining almost as accurate as standard MLNNs.

关键词： CasCor CasPer Neural networks KNN SVM Real world data set Classification autoencoder Genetic algorithm Evolutionary algorithm

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Structural Role Enhanced Attributed Network Embedding 20th

Structural Role Enhanced Attributed Network Embedding

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20th International Conference on Web Information Systems Engineering (WISE)

作者： Li, Zhao Wang, Xin Li, Jianxin Zhang, Qingpeng Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

ISBN: (纸本)9783030342234;9783030342227

In recent years, network embedding methods based on deep learning to process network structure data have attracted widespread attention. It aims to represent nodes in the network as low-dimensional dense real-value vectors and effectively preserve network structure and other valuable information. Most network embedding methods now only preserve the network topology and do not take advantage of the rich attribute information in networks. In this paper, we propose a novel deep attributed network embedding framework (RolEANE), which can preserve network topological structure and attribute information well at the same time. The framework consists of two parts, one of which is the network structural role proximity enhanced deep autoencoder, which is used to capture highly nonlinear network topological structure and attribute information. The other part is that we proposed a neighbor optimization strategy to modify the Skip-Gram model so that it can integrate the network topological structure and attribute information to improve the final embedded performance. The experiments on four real datasets show that our method outperforms other state-of-the-art network embedding methods.

关键词： Network embedding Attributed network autoencoder Structural role proximity

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