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An encoder-decoder Convolution Network With Fine-Grained Spatial Information for Hyperspectral Images Classification

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IEEE ACCESS 2020年 8卷 33600-33608页

作者： Li, Zhongwei Guo, Fangming Li, Qi Ren, Guangbo Wang, Leiquan China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

Convolutional Neural Network (CNN) is widely used in Hyperspectral Images (HSIs) classification. However, the fine-grained spatial (FGS) details are discarded during a sequence of convolution and pooling operations for most of CNN-based HSIs classification methods. To address this issue, a unified encoder-decoder framework is proposed to integrate high-level semantics and FGS details for HSIs classification, denoted by FGSCNN. The encoder, including a series of convolution and pooling layers, captures the high-level semantic information with low resolution feature maps. The decoder fuses the high-level low-resolution semantic and the fine-grained high-resolution spatial information, namely, to get the FGS features with high-level semantics. The deconvolution layers and skip connection are used in the decoder to retain the FGS details, while, convolution layers are also used to combine the FGS features with high-level semantics. Based on the encoder-decoder framework, a unified loss function is exploited to integrate the high-level semantic information and FGS details with an end-to-end manner for HSIs classification. Experiments conducted on the three public datasets, i.e. the Indian Pines, Pavia University and Salinas, demonstrate the effectiveness of the proposed method on HSIs classification.

关键词： Semantics Feature extraction Convolution Decoding Spatial resolution Hyperspectral imaging Machine learning Convolutional neural networks (CNNs) encoder-decoder hyperspectral image (HSI) classification information fusion

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An Effective Classification Method for Hyperspectral Image With Very High Resolution Based on encoder-decoder Architecture

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1509-1519页

作者： Zhang, Zhen Jiang, Tao Liu, Chenxi Zhang, Linjing Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China

Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. However, low interclass spectral variability and high intraclass spectral variability in VHR-HSI, shadows, pedestrians, and low signal-to-noise ratio increase the fuzziness of different categories. To address the known challenges of VHR-HSI classification, an effective classification method based on encoder-decoder architecture is proposed. The proposed algorithm is an object-level contextual convolution neural network based on an improved residual network backbone with 3-D convolution, which fully considers the spatial-spectral and contextual features of HSIs. Two different spatial resolution aerial HSIs are used as experimental data. The results show that the overall accuracy of the proposed method is improved by 7.42% and 18.82%, respectively, compared to the pixelwise convolution neural network and DeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.

关键词： encoder-decoder hyperspectral image (HSI) with high spatial resolution image classification 3-D convolution residual network

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Long-Term Traffic Prediction Based on LSTM encoder-decoder Architecture

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第10期22卷 6561-6571页

作者： Wang, Zhumei Su, Xing Ding, Zhiming Beijing Univ Technol Coll Comp Sci Beijing Adv Innovat Ctr Future Internet Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Beijing Univ Technol Coll Comp Sci Beijing 100124 Peoples R China

Accurate traffic flow prediction is becoming increasingly important for transportation planning, control, management, and information services of successful. Numerous existing models focus on short-term traffic forecasts, but effective long-term forecasting of traffic flows have become a challenging issue in recent years. To solve this problem, this paper proposes a deep learning architecture which consisting of two parts: the long short-term memory encoder-decoder structure at the bottom and the calibration layer at the top. In the encoder-decoder model, we propose an hard attention mechanism based on learning similar patterns to enhance neuronal memory and reduce the accumulation of error propagation. To correct some of the missing details, we design a control gate in the calibration layer to learn the predicted data in groups according to different forms. The proposed method is evaluated on real-world datasets and compared with other state-of-the-art methods. It is verified that our model can accurately learn local feature and long-term dependence, and has better accuracy and stability in long-term sequence prediction.

关键词： Predictive models Forecasting Deep learning Calibration Market research Neural networks Prediction algorithms Freeway traffic flow long-term prediction encoder-decoder similar pattern attention

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Multi-Granularity Sequence Alignment Mapping for encoder-decoder Based End-to-End ASR

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 2816-2828页

作者： Tang, Jian Zhang, Jie Song, Yan McLoughlin, Ian Dai, Li-Rong Univ Sci & Technol China USTC Natl Engn Lab Speech & Language Informat Proc Hefei 230026 Peoples R China

encoder-decoder based automatic speech recognition (ASR) methods are increasingly popular due to their simplified processing stages and low reliance on prior knowledge. Conventional encoder-decoder based approaches usually learn a sequence-to-sequence mapping function from the source speech to target units (e.g., subwords, characters) in an end-to-end manner. However, it is still unclear how to choose the optimal target unit, or granularity of multiple units. In general, as increasing the information available for learning sequence-to-sequence mapping functions can improve modeling effectiveness, we therefore propose a multi-granularity sequence alignment (MGSA) approach. This aims to enhance cross-sequence interactions between different granularity units for both modeling and inference stages in the encoder-decoder based ASR. Specifically, a decoder module is designed to generate multi-granularity sequence predictions. We then exploit the latent alignment mapping among units having different levels of granularity, by utilizing the decoded multi-level sequences as input for model prediction. The cross-sequence interaction can also be employed to re-calibrate output probabilities in the proposed post-inference algorithm. Experimental results on both WSJ-80 hrs and Switchboard-300 hrs datasets show the superiority of the proposed method compared to traditional multi-task methods as well as to single granularity baseline systems.

关键词： Speech processing Multi-granularity sequence alignment end-to-end ASR encoder-decoder post-inference deep learning

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Recurrent and convolutional neural networks in structural dynamics: a modified attention steered encoder-decoder architecture versus LSTM versus GRU versus TCN topologies to predict the response of shock wave-loaded plates

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COMPUTATIONAL MECHANICS 2023年第4期72卷 765-786页

作者： Tandale, Saurabh Balkrishna Stoffel, Marcus Rhein Westfal TH Aachen Inst Gen Mech Aachen Germany

The aim of the present study is to analyse and predict the structural deformations occurring during shock tube experiments with a series of recurrent and temporal convolutional neural networks. The goal is to determine the architecture that can best learn and predict physically and geometrically nonlinear deformations. For this approach, four different architectures are proposed. Firstly, the multi-layered long-short term memory is developed followed by the multi-layered gated recurrent unit (GRU). Both the RNNs allow accounting for history dependent behaviors through their corresponding internal variables. Further, a multilayered temporal convolutional network is initialized, where the dilated convolution operation is responsible for tracing the path dependent behavior. In the mentioned architectures a sequence of mechanical data is passed through the network and a transformation to corresponding displacements is established. These sequences and corresponding deflections belong to a wide range of strain rates in the dynamic response of structures consisting of steel, aluminum, and copper plates including geometrical and physical non-linearities. Finally, an encoder-decoder architecture consisting of GRU layers is introduced with a modified attention mechanism which showed the best result for predicting the dynamic response. Employing comparative calculations between the neural network (NN) enhanced predictions and the measurements, the nature of approximation of each mentioned NN architecture is discussed and the capabilities of these developed surrogate models are demonstrated by its prediction on validation experiments. These validation experiments have displacement and input data ranges beyond the range of data used for training the aforementioned models.

关键词： Recurrent neural network Shock tube experiments encoder-decoder Attention Temporal convolutional network Viscoplasticity

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EDAfuse: A encoder-decoder with atrous spatial pyramid network for infrared and visible image fusion

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IET IMAGE PROCESSING 2023年第1期17卷 132-143页

作者： Nie, Cairen Zhou, Dongming Nie, Rencan Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China Yunnan Normal Univ Sch Econ & Management Kunming Yunnan Peoples R China Southeast Univ Sch Automat Nanjing Peoples R China Yunnan Key Lab Intelligent Syst & Comp Kunming Yunnan Peoples R China

Infrared and visible images come from different sensors, and they have their advantages and disadvantages. In order to make the fused images contain as much salience information as possible, a practical fusion method, termed EDAfuse, is proposed in this paper. In EDAfuse, the authors introduce an encoder-decoder with the atrous spatial pyramid network for infrared and visible image fusion. The authors use the encoding network which includes three convolutional neural network (CNN) layers to extract deep features from input images. Then the proposed atrous spatial pyramid model is utilized to get five different scale features. The same scale features from the two original images are fused by our fusion strategy with the attention model and information quantity model. Finally, the decoding network is utilized to reconstruct the fused image. In the training process, the authors introduce a loss function with saliency loss to improve the ability of the model for extracting salient features from original images. In the experiment process, the authors use the average values of seven metrics for 21 fused images to evaluate the proposed method and the other seven existing methods. The results show that our method has four best values and three second-best values. The subjective assessment also demonstrates that the proposed method outperforms the state-of-the-art fusion methods.

关键词： different scale features feature extraction Sensor fusion original images input images encoding network atrous spatial pyramid network encoder-decoder decoding network Neural nets atrous spatial pyramid model termed EDAfuse Optical, image and video signal processing image fusion Computer vision and image processing techniques practical fusion method convolutional neural nets state-of-the-art fusion methods infrared images object detection convolutional neural network layers deep learning (artificial intelligence) Image recognition visible images fused image information quantity model visible image fusion fusion strategy infrared image fusion

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EDChannel: channel prediction of backscatter communication network based on encoder-decoder

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TELECOMMUNICATION SYSTEMS 2022年第1期81卷 99-114页

作者： Li, Dengao Wen, Yongxin Xu, Shuang Wang, Qiang Bai, Ruiqin Zhao, Jumin Taiyuan Univ Technol Coll Data Sci Jinzhong 030600 Shanxi Peoples R China Taiyuan Univ Technol Coll Informat & Comp Jinzhong 030600 Shanxi Peoples R China Technol Res Ctr Spatial Informat Network Engn Sha Jinzhong 030600 Peoples R China Intelligent Percept Engn Technol Ctr Shanxi Jinzhong 030600 Peoples R China

Backscatter communication networks have attracted much attention due to their small size and low power waste, but their spectrum resources are very limited and are often affected by link bursts. Channel prediction is a method to effectively utilize the spectrum resources and improve communication quality. Most channel prediction methods have failed to consider both spatial and frequency diversity. Meanwhile, there are still deficiencies in the existing channel detection methods in terms of overhead and hardware dependency. For the above reasons, we design a sequence-to-sequence channel prediction scheme. Our scheme is designed with three modules. The channel prediction module uses an encoder-decoder based deep learning model (EDChannel) to predict the sequence of channel indicator measurements. The channel detection module decides whether to perform a channel detection by a trigger that reflects the prediction effect. The channel selection module performs channel selection based on the channel coefficients of the prediction results. We use a commercial reader to collect data in a real environment, and build an EDChannel model based on the deep learning module of Tensorflow and Keras. As a result, we have implemented the channel prediction module and completed the overall channel selection process. The experimental results show that the EDChannel algorithm has higher prediction accuracy than the previous state-of-the-art methods. The overall throughput of our scheme is improved by approximately 2.9% and 14.1% over Zhao's scheme in both stable and unstable environments.

关键词： Backscatter communication Channel prediction Deep learning encoder-decoder

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Action-Aware encoder-decoder Network for Pedestrian Trajectory Prediction

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 20-27页

作者：傅家威赵旭 Department of Automation School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Accurate pedestrian trajectory predictions are critical in self-driving systems,as they are fundamental to the response-and decision-making of ego *** this study,we focus on the problem of predicting the future trajectory of pedestrians from a first-person *** existing trajectory prediction methods from the first-person view copy the bird’s-eye view,neglecting the differences between the *** this end,we clarify the differences between the two views and highlight the importance of action-aware trajectory prediction in the first-person *** propose a new action-aware network based on an encoder-decoder framework with an action prediction and a goal estimation branch at the end of the *** the decoder part,bidirectional long short-term memory(Bi-LSTM)blocks are adopted to generate the ultimate prediction of pedestrians’future *** method was evaluated on a public dataset and achieved a competitive performance,compared with other *** ablation study demonstrates the effectiveness of the action prediction branch.

关键词： pedestrian trajectory prediction first-person view action prediction encoder-decoder bidirectional long short-term memory(Bi-LSTM)

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Learning compact graph representations via an encoder-decoder network

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APPLIED NETWORK SCIENCE 2019年第1期4卷 1页

作者： Lee, John Boaz Kong, Xiangnan Worcester Polytech Inst Worcester MA 01609 USA

Feature representation learning for classification of multiple graphs is a problem with practical applications in many domains. For instance, in chemoinformatics, the learned feature representations of molecular graphs can be used to classify molecules which exhibit anti-cancer properties. In many previous work, including discriminative subgraph mining and graphlet-based approaches, a graph representation is derived by counting the occurrence of various graph sub-structures. However, these representations fail to capture the co-occurrence patterns that are inherently present among different sub-structures. Recently, various methods (e.g., DeepWalk, node2vec) have been proposed to learn representations for nodes in a graph. These methods use node co-occurrence to learn node embeddings. However, these methods fail to capture the co-occurrence relationship between more complex sub-structures in the graph since they were designed primarily for node representation learning. In this work, we study the problem of learning graph representations that can capture the structural and functional similarity of sub-structures (as evidenced by their co-occurrence relationship) in graphs. This is particularly useful when classifying graphs that have very few sub-structures in common. The proposed method uses an encoder-decoder model to predict the random walk sequence along neighboring regions (or sub-structures) in a graph given a random walk along a particular region. The method is unsupervised and can be used to obtain generic feature representations of graphs making it applicable with various types of graphs. We evaluate the learned representations using several real-world datasets on the binary graph classification task. The proposed model is able to achieve superior results against multiple state-of-the-art techniques.

关键词： Deep learning encoder-decoder Graph classification Graph representation RNN

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Dual encoder-decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning

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IEEE ACCESS 2020年 8卷 130159-130171页

作者： Hu, Cong Feng, Zhenhua Wu, Xiaojun Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350121 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

To learn disentangled representations of facial images, we present a Dual encoder-decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose. We further improve the proposed network architecture by minimizing the additional pixel-wise loss defined by the Wasserstein distance at the output of the discriminator so that the adversarial framework can be better trained. Additionally, we consider face pose variation to be continuous, rather than discrete in existing literature, to inject richer pose information into our model. The pose estimation task is formulated as a regression problem, which helps to disentangle identity information from pose variations. The proposed network is evaluated on the tasks of pose-invariant face recognition (PIFR) and face synthesis across poses. An extensive quantitative and qualitative evaluation carried out on several controlled and in-the-wild benchmarking datasets demonstrates the superiority of the proposed DED-GAN method over the state-of-the-art approaches.

关键词： Face Gallium nitride Generative adversarial networks Training Generators Face recognition Task analysis Disentangled representation learning encoder-decoder generative adversarial networks face synthesis pose invariant face recognition

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