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检索条件"主题词=autoencoder"

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Image classification using quantum inference on the D-Wave 2X

Image classification using quantum inference on the D-Wave 2...

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3rd IEEE International Conference on Rebooting Computing (ICRC)

作者： Nguyen, Nga T. T. Kenyon, Garrett T. Los Alamos Natl Lab Informat Sci Los Alamos NM 87545 USA New Mexico Consortium Los Alamos NM 87545 USA

ISBN: (纸本)9781538691700

We use a quantum annealing D-Wave 2X computer to obtain solutions to NP-hard sparse coding problems. To reduce the dimensionality of the sparse coding problem to fit on the quantum D-Wave 2X hardware, we passed downsampled MNIST images through a bottleneck autoencoder. To establish a benchmark for classification performance on this reduced dimensional data set, we built two deep convolutional neural networks (DCNNs). The first DCNN used an AlexNet-like architecture and the second a state-of-the-art residual network (RESNET) model, both implemented in TensorFlow. The two DCNNs yielded classification scores of 94.54 +/- 0.7% and 98.8 +/- 0.1%, respectively. As a control, we showed that both DCNN architectures produced near-state-of-the-art classification performance (similar to 99%) on the original MNIST images. To obtain a set of optimized features for inferring sparse representations of the reduced dimensional MNIST dataset, we imprinted on a random set of 47 image patches followed by an off-line unsupervised learning algorithm using stochastic gradient descent to optimize for sparse coding. Our single-layer of sparse coding matched the stride and patch size of the first convolutional layer of the AlexNet-like DCNN and contained 47 fully-connected features, 47 being the maximum number of dictionary elements that could be embedded onto the D-Wave 2X hardware. When the sparse representations inferred by the D-Wave 2X were passed to a linear support vector machine, we obtained a classification score of 95.68%. We found that the classification performance supported by quantum inference was maximal at an optimal level of sparsity corresponding to a critical value of the sparsity/reconstruction error trade-off parameter that previous work has associated with a second order phase transition, an observation supported by a free energy analysis of D-Wave energy states. We mimicked a transfer learning protocol by feeding the D-Wave representations into a multilayer perceptron

关键词： Sparse coding Neuromorphic computing MNIST Quantum annealing D-Wave 2X Deep Convolutional Neural Networks autoencoder

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Auto-Encoder with Neural Networks for Wind Speed Forecasting

Auto-Encoder with Neural Networks for Wind Speed Forecasting

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International Conference on Communications and Electrical Engineering (ICCEE)

作者： Mezaache, Hatem Bouzgou, Hassen Univ Msila Dept Elect Lab GE Msila Algeria Univ Batna 2 Dept Ind Engn Batna Algeria

ISBN: (纸本)9781728101125

The use of wind energy is progressively utilized to produce electrical energy. Wind energy is related to the variation of some atmospheric variables such as wind direction, wind speed, air density and atmospheric pressure. Recently, numerous methods base on Artificial Intelligence techniques to forecast wind speed have been proposed in the literature. In this paper a new artificial intelligence approach for wind speed time series forecasting is proposed, it is composed from two blocs: The first one is based on the use of a deep architecture. The autoencoder which is a type of deep neural networks, utilized generally for Denoising, is employed to reduce the wind speed input dimensionality. In the second bloc of the proposed methodology, the Elman neural network is employed to forecast future values of wind series, it is a kind of recurrent neural networks that are very sensitive to historical variations. To evaluate our approach we used the following error indicators: Root Mean Square Error (RMSE), Mean Absolute Bias Error (MABE), Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R-2). The obtained results are compared with those of the Extreme Learning Machine method.

关键词： Deep neural networks autoencoder Recurrent Neural Networks Elman Neural Network Extreme Learning Machine Wind Speed Time series

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Segmentation of Thermal Breast Images Using Convolutional and Deconvolutional Neural Networks

Segmentation of Thermal Breast Images Using Convolutional an...

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IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Guan, Shuyue Kamona, Nada Loew, Murray George Washington Univ Med Ctr Dept Biomed Engn Washington DC 20037 USA

ISBN: (纸本)9781538693063

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer has been shown to be the key to higher survival rates for breast cancer patients. We are investigating infrared thermography as a noninvasive adjunctive to mammography for breast screening. Thermal imaging is safe, radiation-free, pain-free, and non-contact. Segmentation of breast area from the acquired thermal images will help limit the area for tumor search and reduce the time and effort needed for manual hand segmentation. autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) are promising computational approaches to automatically segment breast areas in thermal images. In this study, we apply the C-DCNN to segment breast areas from our thermal breast images database, which we are collecting in our clinical trials by imaging breast cancer patients with our infrared camera (N2 Imager). For training the C-DCNN, the inputs are 132 gray-value thermal images and the corresponding manually-cropped breast area images (binary masks to designate the breast areas). For testing, we input thermal images to the trained C-DCNN and the output after post-processing are the binary breast-area images. Cross-validation and comparison with the ground-truth images show that the C-DCNN is a promising method to segment breast areas. The results demonstrate the capability of C-DCNN to learn essential features of breast regions and delineate them in thermal images.

关键词： breast thermogram segmentation convolutional neural networks deep learning deconvolutional neural networks infrared thermography computer aided diagnosis autoencoder

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A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

A Visual Interaction Framework for Dimensionality Reduction ...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Cavallo, Marco Demiralp, Cagatay IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781450356206

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data, We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.

关键词： Dimensionality reduction interaction bidirectional binding visual embedding forward projection backward projection PCA autoencoder deep learning proline feasibility map exploratory data analysis what-if analysis Praxis

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UNDERSTANDING THE AESTHETIC STYLES OF SOCIAL IMAGES

UNDERSTANDING THE AESTHETIC STYLES OF SOCIAL IMAGES

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

作者： Ma, Yihui Jia, Jia Hou, Yufan Bu, Yaohua Han, Wentao Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Minist Educ Key Lab Pervas Comp Tsinghua Natl Lab Informat Sci & Technol TNList Beijing Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China

ISBN: (纸本)9781538646588

Aesthetic perception is nearly the most direct impact people could receive from images. Recent research on image understanding is mainly focused on image analysis, recognition and classification, regardless of the aesthetic meanings embedded in images. In this paper, we systematically study the problem of understanding the aesthetic styles of social images. First, we build a two-dimensional Image Aesthetic Space (IAS) to describe image aesthetic styles quantitatively and universally. Then, we propose a Bimodal Deep autoencoder with Cross Edges (BDA-CE) to deeply fuse the social image related features (i.e. images' visual features, tags' textual features). Connecting BDA-CE with a regression model, we are able to map the features to the IAS. The experimental results on the benchmark dataset we build with 120 thousand Flickr images show that our model outperforms (+ 5.5% in terms of MSE) alternative baselines. Furthermore, we conduct an interesting case study to demonstrate the advantages of our methods.

关键词： Aesthetic style social image autoencoder dimensional space

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A Surface Defect Detection Method Based on Positive Samples 15th

A Surface Defect Detection Method Based on Positive Samples

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15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW)

作者： Zhao, Zhixuan Li, Bo Dong, Rong Zhao, Peng Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Huichuan Image Vis Technol Co Ltd Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319973104;9783319973098

Surface defect detection and classification based on machine vision can greatly improve the efficiency of industrial production. With enough labeled images, defect detection methods based on convolution neural network have achieved the detection effect of state-of-art. However in practical applications, the defect samples or negative samples are usually difficult to be collected beforehand and manual labelling is time-consuming. In this paper, a novel defect detection framework only based on training of positive samples is proposed. The basic detection concept is to establish a reconstruction network which can repair defect areas in the samples if they are existed, and then make a comparison between the input sample and the restored one to indicate the accurate defect areas. We combine GAN and autoencoder for defect image reconstruction and use LBP for image local contrast to detect defects. In the training process of the algorithm, only positive samples is needed, without defect samples and manual label. This paper carries out verification experiments for concentrated fabric images and the dataset of DAGM 2007. Experiments show that the proposed GAN+LBP algorithm and supervised training algorithm with sufficient training samples have fairly high detection accuracy. Because of its unsupervised characteristics, it has higher practical application value.

关键词： Positive samples Surface defect detection autoencoder GAN

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Cost-Effective Signal Map Crowdsourcing with Auto-Encoder Based Active Matrix Completion

Cost-Effective Signal Map Crowdsourcing with Auto-Encoder Ba...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chengyong Liu Yanchao Zhao Kun Zhu Sheng Zhang Jie Wu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key laboratory for Novel Software Technology Nanjing University China Department of Computer and Information Sciences Temple University USA

ISBN: (纸本)9781728125848

Signal map is of great importance, especially in the dawn of 5G network, for site spectrum monitoring, location-based services (LBS), network construction, and cellular planning. Despite its significance, the traditional signal map construction, e.g., through full site survey, could be time-consuming and labor-intensive as the signal varies frequently over time and the accuracy requirement grows rapidly with the emergence of new applications. Even with crowdsourcing scheme, the participants tend to be unevenly distributed in space while the encouragement budgets for the participants could be far from enough to collect adequate high-quality measurements. Therefore, the signal map constructed by crowdsourcing is often sparse and incomplete. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with minimum cost and propose an auto-encoder-based active signal map reconstruction method (AER). Our method is mainly innovative in three parts. Firstly, AER can effectively update the signal map with only a small number of observations while also fully using the incomplete historical signals to effectively update the signal map online. Secondly, AER consists of an active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction, which further reduces the measurement cost to a large extent. Thirdly, to cope with the measurement dynamics, we give a new signal map model describing not only the signal strength but also the signal dynamics, based on which an advanced AER algorithm is proposed. The simulation results demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

关键词： Signal map Active matrix completion autoencoder Crowdsourcing Signal mapping signals maps MAPPING(MATHEMATICAL) Crowdsource ANION EXCHANGE RESIN Site surveys required precision

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Deep Learning Crop Classification Approach Based on Coding Input Satellite Data Into the Unified Hyperspace 38

Deep Learning Crop Classification Approach Based on Coding I...

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IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)

作者： Lavreniuk, Mykola Kussul, Nataliia Novikov, Alexei Natl Tech Univ Ukraine Dept Space Informat Technol & Syst Space Res Inst NASU SSAU Dept Informat SecurIgor Sikorsky Kyiv Polytech I Kiev Ukraine Natl Tech Univ Ukraine Dept Informat Secur Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

ISBN: (纸本)9781538663837

To provide reliable crop maps for the same territory each year, it is necessary to collect in-situ data for each year independently. Collecting ground truth data is a very time consuming and challenging task. At present, unfortunately, there is no an adopted approach, how to utilize in-situ and satellite data from previous years for crop mapping in the subsequent years. In this paper, we propose a new deep learning approach using sparse autoencoder based on only satellite data, and a further procedure of neural network fine-tuning based on in-situ data. The possibility of utilizing this deep learning architecture based on translating all available satellite data into the unified hyperspace. The study is carried out for the central part of Ukraine. Obtained results show that this technique is feasible and provides reliable crop classification maps with overall accuracy (OA) of 91.0% and 85.9% for two different experiments. The use of the proposed approach makes it possible to avoid, or decrease, the necessity for collecting in-situ data for each year and for each part of large territory.

关键词： sparse coding autoencoder neural network fine-tuning crop mapping Sentinel-1

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Anomaly Detection in Manufacturing Systems Using Structured Neural Networks 13

Anomaly Detection in Manufacturing Systems Using Structured ...

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13th World Congress on Intelligent Control and Automation (WCICA)

作者： Liu, Jie Guo, Jianlin Orlik, Philip Shibata, Masahiko Nakahara, Daiki Mii, Satoshi Takac, Martin Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA MERL Cambridge MA 02139 USA Mitsubishi Electr Corp IT R&D Ctr Kamakura Kanagawa 2478501 Japan

ISBN: (纸本)9781538673454

This paper proposes innovative anomaly detection technologies for manufacturing systems. We combine the event ordering relationship based structuring technique and the deep neural networks to develop the structured neural networks for anomaly detection. The event ordering relationship based neural network structuring process is performed before neural network training process and determines important neuron connections and weight initialization. It reduces the complexity of the neural networks and can improve anomaly detection accuracy. The structured time delay neural network (TDNN) is introduced for anomaly detection via supervised learning. To detect anomaly through unsupervised learning, we propose the structured autoencoder. The proposed structured neural networks outperform the unstructured neural networks in terms of anomaly detection accuracy and can reduce test error by 20%. Compared with popular methods such as one-class SVM, decision trees, and distance-based algorithms, our structured neural networks can reduce anomaly detection misclassification error by as much as 64%.

关键词： Anomaly detection manufacturing system machine learning time delay neural network autoencoder

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Clustering of Astronomical Transient Candidates Using Deep Variational Embedding

Clustering of Astronomical Transient Candidates Using Deep V...

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International Joint Conference on Neural Networks (IJCNN)

作者： Astorga, Nicolas Huijse, Pablo Estevez, Pablo A. Foerster, Francisco Univ Chile Dept Elect Engn Santiago Chile Millennium Inst Astrophys Santiago Chile Univ Chile Ctr Math Modeling Santiago Chile

ISBN: (纸本)9781509060146

The exponential growth of the data collected by telescopes have turned astronomy into a data-drive science. The detection of astronomical transient events, short-lived and bright phenomena such as the Supernovae, is currently a main science driver of many astronomical surveys. There is an opportunity for the application of machine learning methods for the automatic detection of astronomical transients. In this paper we focus on the unsupervised learning case to perform an exploratory analysis on a dataset of 1,250,000 astronomical transient candidates from the High Cadence Transient Survey. Our contributions can be summarized in 1) The application of Deep Variational Embedding for latent space clustering of a large database of transient candidates obtaining a clustering accuracy of 95:33% and 2) The proposal of an auto-regularization term as a novel approach to solve the common problem of over-regularization in variational autoencoders, we show that using this term not only improves the convergence of the algorithm but also increases the clustering accuracy and reconstruction quality.

关键词： autoencoder variational inference clustering astronomical images transients

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