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Online encoder-decoder Anomaly Detection using encoder-decoder Architecture with Novel Self-configuring Neural Networks & Pure Linear Genetic Programming for Embedded Systems 11th

Online Encoder-decoder Anomaly Detection using Encoder-decod...

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11th International Joint Conference on Computational Intelligence (IJCCI)

作者： Kasparaviciute, Gabriele Thelin, Malin Nordin, Peter Soderstam, Per Magnusson, Christian Almljung, Mattias Chalmers Univ Technol Chalmersplatsen 4 Gothenburg Sweden Univ Gothenburg Rannvagen 6B Gothenburg Sweden Semcon AB Lindholmsallen 2 Gothenburg Sweden

ISBN: (纸本)9789897583841

Recent anomaly detection techniques locus on the use of neural networks and an encoder-decoder architecture. However, these techniques lead to trade offs if implemented in an embedded environment such as high heat management, power consumption and hardware costs. This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus. The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP). Second method uses self-configuring neural network that is created using evolutionary algorithm paradigm learning both architecture and weights suitable for embedded systems. Both approaches have the following advantages: it is inexpensive regarding resource use, can be run on almost any embedded board due to linear register machine advantages in computation. The proposed methods are also faster by at least one order of magnitude, and it includes both inference and complete training.

关键词： encoder-decoder Anomaly Detection Linear Genetic Programming Evolutionary Algorithm Genetic Algorithm Embedded Self-configuring Neural Network

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AUTODEPTH: SINGLE IMAGE DEPTH MAP ESTIMATION VIA RESIDUAL CNN encoder-decoder AND STACKED HOURGLASS 26

AUTODEPTH: SINGLE IMAGE DEPTH MAP ESTIMATION VIA RESIDUAL CN...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Kumari, Seema Jha, Ranjeet Ranjhan Bhavsar, Arnav Nigam, Aditya Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9781538662496

We address the task of estimating depth from a single intensity image via a novel convolutional neural network (CNN) encoder-decoder architecture, which learns the depth information using example pairs of color images and their corresponding depth maps. The proposed model integrates residual connections within pooling and up-sampling layers, and hourglass networks which operate on the encoded features, thus processing these at various scales. Furthermore, the model is optimized under the constraints of perceptual as well as the mean squared error loss. The perceptual loss considers the high-level features, thus operating at a different scale of abstraction, which is complementary to the mean squared error loss. The improvements in qualitative and quantitative comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach, even in presence of noise.

关键词： Depth map estimation CNN Residual connection encoder-decoder Hourglass

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encoder-decoder with double spatial pyramid for semantic segmentation

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JOURNAL OF ELECTRONIC IMAGING 2019年第6期28卷 063007-063007页

作者： Kong, Huifang Hu, Jie Fan, Lei Zhang, Xiaoxue Fang, Yao Hefei Univ Technol Sch Elect Engn & Automat Hefei Anhui Peoples R China

Semantic segmentation, as a dense pixelwise classification task, is of great significance to scene understanding. Many approaches based on convolutional neural network still suffer from two kinds of challenges: (1) insufficient semantic information results in semantic obfuscation between similar categories, (2) loss of spatial information leads to inaccurate location of inconspicuous objects. To tackle these challenges, we design a network with an encoder-decoder architecture based on two proposed modules: global pyramid attention module (GPAM) and pyramid decoder module (PDM). Specifically, GPAM exploits an attention mechanism as global prior knowledge to adaptively capture discriminative features for enhancing semantic representation, and PDM employs small convolutions connected in parallel to predict adjacent position relationships for refining spatial information. A series of ablation experiments are conducted to demonstrate the effectiveness of our designs, and our network achieves a mean intersection over union score of 83.4% on PASCAL VOC 2012 dataset and 78.5% on Cityscapes dataset. (C) 2019 SPIE and IS&T

关键词： semantic segmentation encoder-decoder spatial pyramid attention mechanism

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GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent encoder-decoder Architecture with Attention Mechanism

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SENSORS 2020年第18期20卷 5143.-5143.页

作者： Nawaz, Asif Huang, Zhiqiu Wang, Senzhang Akbar, Azeem AlSalman, Hussain Gumaei, Abdu Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Technol Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Safety Crit Software Minist Ind & Informat Technol Nanjing 211106 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210093 Peoples R China King Saud Univ Dept Comp Sci Coll Comp & Informat Sci Riyadh 11543 Saudi Arabia

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete;this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics;this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.

关键词： GPS trajectory ConvLSTM encoder-decoder attention trajectory completion

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Optimizing FPGA-based Convolutional encoder-decoder Architecture for Semantic Segmentation 9

Optimizing FPGA-based Convolutional Encoder-Decoder Architec...

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9th IEEE Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER)

作者： Yu, Mengqi Huang, Hongzhi Liu, Hong He, Shuyi Qiao, Fei Luo, Li Xie, Fugui Liu, Xin-Jun Yang, Huazhong Tsinghua Univ Dept Elect Engn BNRist Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Tsinghua Univ Dept Mech Engn Beijing Peoples R China

ISBN: (纸本)9781728107707

Convolutional neural networks (CNNs) for visual semantic segmentation have been attracting considerable attention recently because of their superior support for many significant tasks, such as autonomous driving, semantic SLAM (simultaneous localization and mapping) and remote sensing surveying and mapping. These kinds of applications generally need to he implemented on the smart terminals, which means that a kind of hardware platform with high energy efficiency and real-time performance is required. However, CNNs for semantic segmentation usually contain sonic, symmetrical encoders and decoders, corresponding to the down-sampling process (e.g., pooling, convolution) and the up-sampling process (e.g., unpooling, deconvolution). All of these processes are computing and storage intensive, which limits their applicability in the resource constrained embedded systems. In this paper, an FPGA-based accelerator programed by OpenCL is proposed. We evaluate its performance on the CamVid dataset. The global accuracy only drops by 2.04% with 8-bit quantization. Additionally, the system shows 48.89 GOPS and 2.4x real-time performance against CPU when running on an Arria-10 GX1150 device.

关键词： FPGA Convolutional Neural Networks encoder-decoder Semantic Segmentation Accelerator

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Image Semantic Segmentation Based on encoder-decoder Network

Image Semantic Segmentation Based on Encoder-Decoder Network

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作者： Xiaopin Zhao Weibin Liu Weiwei Xing Institute of Information Science Beijing Jiaotong University School of Software Engineering Beijing Jiaotong University

Semantic segmentation is an extremely important task in computer vision. At present, the related methods have achieved high performance. Nevertheless, Semantic segmentation still faces the challenge of localization accuracy due to DCNN invariance and existence of objects at multi-scale. In order to improve the accuracy of segmentation, this paper proposes a U-SEM encoderdecoder network. Firstly, in the encoding stage, it down-samples through the ResNet. Secondly, in the decoding stage, in order to filter and utilize the useful features, the SE-Mobile Block is proposed and fused to the network. The SE block adopts the idea of attention mechanism to focus on useful features and ignore those redundant features. Mobile blocks use deep separable convolutions to replace traditional convolutions, speeding up operations and reducing parameters. Finally, it adopts the skip structure where the feature information of different scales are merged to produce accurate and detailed segmentation. Experimental results show that the proposed network achieves good performance on multiple datasets which reaches the accuracy of 78.4% m IOU on PASCAL VOC 2012 and 75.7% mIOU on Cityscapes dataset.

关键词： encoder-decoder Semantic segmentation Attention mechanism Mobile block

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Multi scale mirror connection based encoder decoder network for text localization

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PATTERN RECOGNITION LETTERS 2020年 135卷 64-71页

作者： Dutta, Kalpita Bal, Malyaban Basak, Arpita Ghosh, Swarnendu Das, Nibaran Kundu, Mahantapas Nasipuri, Mita Jadavpur Univ Dept CSE Kolkata 700032 WB India RCC Inst Technol Dept CSE Kolkata 700015 WB India

encoder decoder models with multi-scale feature concatenations have become ubiquitous for various natural scene segmentation tasks. In the current approach, a similar model with an improved mirror connection from encoders to decoder has been proposed. Three different types of mirror connections, namely, linear, parametric and convolutional, have been demonstrated in the proposed work. We have also implemented the use of internal skips to facilitate better gradient propagation within the encoder-decoder architecture. The proposed model also consists of an ensemble module that combines outputs from models with different kernel sizes, such as, 3 × 3, 5 × 5 and 7 × 7 to combine multi-scale features for efficient detections. The model was tested on the ICDAR 2003, SVT, ICDAR 2015 and the Total-Text dataset where it proved to be superior to other state of the art encoder-decoder architectures for pixel level classification.

关键词： Scene image Segmentation encoder-decoder Mirror skip

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An LSTM based encoder-decoder Model for Multi-Step Traffic Flow Prediction

An LSTM based Encoder-Decoder Model for Multi-Step Traffic F...

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International Joint Conference on Neural Networks (IJCNN)

作者： Du, Shengdong Li, Tianrui Yang, Yan Gong, Xun Homg, Shi-Jinn Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Sch Informat Sci & Technol Chengdu Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781728119854

Traffic flow prediction has been regarded as a key research problem in the intelligent transportation system. In this paper, we propose an encoder-decoder model with temporal attention mechanism for multi-step forward traffic flow prediction task, which uses LSTM as the encoder and decoder to learn the long dependencies features and nonlinear characteristics of multivariate traffic flow related time series data, and also introduces a temporal attention mechanism for more accurately traffic flow prediction. Through the real traffic flow dataset experiments, it has shown that the proposed model has better prediction ability than classic shallow learning and baseline deep learning models. And the predicted traffic flow value can be well matched with the ground truth value not only under short step forward prediction condition but also under longer step forward prediction condition, which validates that the proposed model is a good option for dealing with the realtime and forward-looking problems of traffic flow prediction task.

关键词： traffic flow prediction long short-term memory networks encoder-decoder temporal attention mechanism

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Arabic To Bangla Machine Translation Using encoder decoder Approach

Arabic To Bangla Machine Translation Using Encoder Decoder A...

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IEEE-Region-10 Symposium (TENSYMP) - Technology for Impactful Sustainable Development

作者： Kamruzzaman, Toshiba Rajshahi Univ Engn & Technol Elect & Comp Engn Rajshahi Bangladesh

ISBN: (纸本)9781728173665

Machine Translation is one of the sub-fields of computational linguistics which includes broad analytical models enlarged by using extremely advanced semantic *** have converted the Arabic sentences to its corresponding Bangla equivalent sentences using a Neural Machine Translation (NMT) system operated with fixed vocabulary by using Sequence to Sequence (Seq2Seq) mechanism which is structured approach for automatically transferring the source sequences to the target sequences. From experiments,we have achieved 69.76% accuracy in this discipline.

关键词： Machine Translation Arabic to Bangla Sequential to Sequential encoder-decoder RNN

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encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation 1

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15th European Conference on Computer Vision (ECCV)

作者： Chen, Liang-Chieh Zhu, Yukun Papandreou, George Schroff, Florian Adam, Hartwig Google Inc Mountain View CA 94043 USA

ISBN: (数字)9783030012342

ISBN: (纸本)9783030012342;9783030012335

Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at https://***/tensorflow/models/tree/master/research/deeplab.

关键词： Semantic image segmentation Spatial pyramid pooling encoder-decoder Depthwise separable convolution

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