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Discriminative Embedding autoencoder With a Regressor Feedback for Zero-Shot Learning

IEEE ACCESS 2020年 8卷 11019-11030页

作者： Shi, Ying Wei, Wei Beihang Univ LMIB Beijing 100191 Peoples R China Beihang Univ Sch Math & Syst Sci Beijing 100191 Peoples R China

Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes. Some typical models are to learn the proper embedding between the image feature space and the semantic space, whilst it is important to learn discriminative features and comprise the coarse-to-fine image feature and semantic information. In this paper, we propose a discriminative embedding autoencoder with a regressor feedback (DEARF) model for ZSL. The encoder learns a mapping from the image feature space to the discriminative embedding space, which regulates both inter-class and intra-class distances between the learned features by a margin, making the learned features be discriminative for object recognition. The regressor feedback learns to map the reconstructed samples back to the the discriminative embedding and the semantic embedding, assisting the decoder to improve the quality of the samples and provide a generalization to the unseen classes. The DEARF model is validated extensively on the benchmark datasets, and the experiment results show that the DEARF model outperforms the state-of-the-art models, and especially in the generalized zero-shot learning (GZSL), significant improvements are achieved.

关键词： Zero-shot learning discriminative embedding autoencoder regressor feedback

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Flow field prediction in bed configurations: A parametric spatio-temporal convolutional autoencoder approach

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NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS 2024年

作者： Mjalled, Ali Namdar, Reza Reineking, Lucas Norouzi, Mohammad Varnik, Fathollah Moennigmann, Martin Ruhr Univ Bochum Automat Control & Syst Theory D-44801 Bochum Germany Ruhr Univ Bochum Interdisciplinary Ctr Adv Mat Simulat Bochum Germany

The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. Here we present a data-driven reduced-order model (ROM) for predicting flow fields in a bed configuration of hot particles. The ROM consists of a parametric spatio-temporal convolutional autoencoder. The neural network architecture comprises two main components. The first part resolves the spatial and temporal dependencies present in the input sequence, while the second part of the architecture is responsible for predicting the solution at the subsequent timestep based on the information gathered from the preceding part. We also propose the utilization of a post-processing non-trainable output layer following the decoding path to incorporate the physical knowledge, e.g. no-slip condition, into the prediction. The ROM is evaluated by comparing its predicted solution with the high-fidelity counterpart. In addition, proper orthogonal decomposition (POD) is employed to systematically analyze and compare the dominant structures present in both sets of solutions. The assessment of the ROM for a bed configuration with variable particle temperature showed accurate results at a fraction of the computational cost required by traditional numerical simulation methods.

关键词： autoencoder lattice Boltzmann neural networks reduced-order model

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

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Frontiers of Mechanical Engineering 2021年第4期16卷 829-839页

作者： Jie LIU Kaibo ZHOU Chaoying YANG Guoliang LU School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Engineering Shandong UniversityJinan 250061China

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health ***,the collection of fault signals is very difficult and expensive,resulting in the problem of imbalanced training *** will degrade the performance of fault diagnosis methods *** address this problem,an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this *** autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the *** the edge connections in the graph depend on the relationship between *** the basis,graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating *** experiments are conducted on a benchmarking publicized dataset and a practical experimental platform,and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

关键词： imbalanced fault diagnosis graph feature learning rotating machinery autoencoder

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History Matching of Naturally Fractured Reservoirs Using a Deep Sparse autoencoder

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SPE JOURNAL 2021年第4期26卷 1700-1721页

作者： Zhang, Kai Zhang, Jinding Ma, Xiaopeng Yao, Chuanjin Zhang, Liming Yang, Yongfei Wang, Jian Yao, Jun Zhao, Hui China Univ Petr Qingdao Peoples R China Yangtze Univ Jingzhou Hebei Peoples R China

Although researchers have applied many methods to history matching, such as Monte Carlo methods, ensemble-based methods, and optimization algorithms, history matching fractured reservoirs is still challenging. The key challenges are effectively representing the fracture network and coping with large amounts of reservoir-model parameters. With increasing numbers of fractures, the dimension becomes larger, resulting in heavy computational work in the inversion of fractures. This paper proposes a new characterization method for the multiscale fracture network, and a powerful dimensionality-reduction method by means of an autoencoder for model parameters. The characterization method of the fracture network is dependent on the length, orientation, and position of fractures, including largescale and small-scale fractures. To significantly reduce the dimension of parameters, the deep sparse autoencoder (DSAE) transforms the input to the low-dimensional latent variables through encoding and decoding. Integrated with the greedy layer-wise algorithm, we set up a DSAE and then take the latent variables as optimization variables. The performance of the DSAE with fewer activating nodes is excellent because it reduces the redundant information of the input and avoids overfitting. Then, we adopt the ensemble smoother (ES) with multiple data assimilation (ES-MDA) to solve this minimization problem. We test our proposed method in three synthetic reservoir history-matching problems, compared with the no-dimensionality-reduction method and the principal-component analysis (PCA). The numerical results show that the characterization method integrated with the DSAE could simplify the fracture network, preserve the distribution of fractures during the update, and improve the quality of history matching naturally fractured reservoirs.

关键词： evolutionary algorithm machine learning optimization problem complex reservoir history matching autoencoder upstream oil & gas oil-saturation map artificial intelligence neural network

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Sahand: A Software Fault-Prediction Method Using autoencoder Neural Network and K-Means Algorithm

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2024年第2期40卷 229-243页

作者： Arasteh, Bahman Golshan, Sahar Shami, Shiva Kiani, Farzad Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Islamic Azad Univ Dept Software Engn Tabriz Branch Tabriz Iran Seraj Inst Dept Software Engn Tabriz Azerbaijan Iran Fatih Sultan Mehmet Vakif Univ Data Sci Applicat & Res Ctr VEBIM Istanbul Turkiye

Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study.

关键词： Software Fault Prediction Clustering autoencoder K-means Accuracy

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Deep manifold matrix factorization autoencoder using global connectivity for link prediction

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APPLIED INTELLIGENCE 2023年第21期53卷 25816-25835页

作者： Lin, Xinyi Chen, Xiaoyun Zheng, Zhiwen Fuzhou Univ Coll Math & Stat Fuzhou 350116 Peoples R China

Link prediction aims to predict missing links or eliminate spurious links by employing known complex network information. As an unsupervised linear feature representation method, matrix factorization (MF)-based autoencoder (AE) can project the high-dimensional data matrix into the low-dimensional latent space. However, most of the traditional link prediction methods based on MF or AE adopt shallow models and single adjacency matrices, which cannot adequately learn and represent network features and are susceptible to noise. In addition, because some methods require the input of symmetric data matrix, they can only be used in undirected networks. Therefore, we propose a deep manifold matrix factorization autoencoder model using global connectivity matrix, called DM-MFAE-G. The model utilizes PageRank algorithm to get the global connectivity matrix between nodes for the complex network. DM-MFAE-G performs deep matrix factorization on the local adjacency matrix and global connectivity matrix, respectively, to obtain global and local multi-layer feature representations, which contains the rich structural information. In this paper, the model is solved by alternating iterative optimization method, and the convergence of the algorithm is proved. Comprehensive experiments on different real networks demonstrate that the global connectivity matrix and manifold constraints introduced by DM-MFAE-G significantly improve the link prediction performance on directed and undirected networks.

关键词： Link prediction Graph representation learning Deep matrix factorization Manifold regularization autoencoder

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Efficient convolutional hierarchical autoencoder for human motion prediction

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VISUAL COMPUTER 2019年第6-8期35卷 1143-1156页

作者： Li, Yanran Wang, Zhao Yang, Xiaosong Wang, Meili Poiana, Sebastian Iulian Chaudhry, Ehtzaz Zhang, Jianjun Bournemouth Univ Natl Ctr Comp Animat Bournemouth Dorset England Bournemouth Univ Natl Ctr Internet Things IoT Cyber Secur Bournemouth Dorset England Bournemouth Univ Natl Ctr Comp Animat Comp Graph Bournemouth Dorset England Nanjing Inst Adv Artificial Intelligence Nanjing Jiangsu Peoples R China Horizon Robot Nanjing Jiangsu Peoples R China Northwest A&F Univ Coll Informat Engn Xian Shaanxi Peoples R China

Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.

关键词： Motion prediction Deep learning autoencoder Hierarchical networks

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Improved autoencoder Model With Memory Module for Anomaly Detection

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IEEE SENSORS JOURNAL 2024年第8期24卷 12770-12781页

作者： Huang, Wei Liu, Zhen Jin, Xiaohang Xu, Jinshan Yao, Xinwei Zhejiang Univ Technol Coll Comp Sci Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China

As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In addition, it is well known that the autoencoder model has over-prominent reconstruction ability for anomalous data, leading to high false-negative rate. On the other hand, the deep support vector data description (SVDD) model first extracts features through deep neural network, and then map the extracted features into a hypersphere. However, the deep SVDD model has disadvantages such as feature information loss and feature collapse during training process, leading to a decrease in anomaly detection accuracy. To alleviate such drawbacks mentioned above, in this article, we propose a novel model, called improved autoencoder with memory module (IAEMM). Specifically, this model jointly learns deep SVDD model and autoencoder model to minimize the overall loss of deep SVDD error and reconstruction error, and add a memory module after encoder to amplify the difference of reconstruction error between normal and abnormal data. The proposed model can well identify abnormal hidden features and mitigate the problem of feature collapse. Experimental results on several datasets confirm the effectiveness and stability of our proposed method.

关键词： Anomaly detection autoencoder deep support vector data description (SVDD) memory

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An Efficient and Robust Underwater Image Compression Scheme Based on autoencoder

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IEEE JOURNAL OF OCEANIC ENGINEERING 2023年第3期48卷 925-945页

作者： Liu, Jiahui Yuan, Fei Xue, Chang Jia, Zhenyu Cheng, En Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informat Minist Educ Xiamen 361005 Peoples R China Xiamen Univ Sch Elect Sci & Engn Xiamen 361005 Peoples R China

Images occupy a prominent place in data because they are more visually appealing than sounds or texts. Acoustic waves are the only feasible alternative for long-distance underwater transmission since seawater has a high absorption impact on lighting and electromagnetic signals. However, underwater acoustic (UWA) communication technology can only provide relatively limited bandwidth (low effectiveness) and insufficiently stable links (low reliability). As a result, it is challenging to send high-resolution underwater images via the UWA channel. This article proposes an effective and robust underwater image compression scheme. First, an autoencoder is used for underwater image extreme bit rate compression. Then, a multistep training strategy is proposed to improve the robustness of the decoder by gradually learning channel degradation features. Finally, the autoencoder encodes images in two paths to achieve efficient compression and higher image quality when reconstructing images. The main path completes the compression task with a low bit rate and high robustness, while the branching path implements the image block retransmission compensation through the feedback signal. The experimental results demonstrate that the content of the reconstructed image can still be recognized under the conditions of a compression ratio of up to 1/768 and an average bit error rate of up to 10(-1). The joint multistep training strategy and multidescription coding achieve a low bit rate and high robustness for underwater image communication, which has good application prospects.

关键词： Image coding Training Quantization (signal) Robustness OFDM Image reconstruction Channel coding autoencoder deep learning underwater communication underwater image compression

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Shared autoencoder Gaussian Process Latent Variable Model for Visual Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第9期29卷 4272-4286页

作者： Li, Jinxing Zhang, Bob Zhang, David Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Univ Macau Dept Comp & Informat Sci Zhuhai 999078 Peoples R China

Multiview learning reveals the latent correlation among different modalities and utilizes the complementary information to achieve a better performance in many applications. In this paper, we propose a novel multiview learning model based on the Gaussian process latent variable model (GPLVM) to learn a set of nonlinear and nonparametric mapping functions and obtain a shared latent variable in the manifold space. Different from the previous work on the GPLVM, the proposed shared autoencoder Gaussian process (SAGP) latent variable model assumes that there is an additional mapping from the observed data to the shared manifold space. Due to the introduction of the autoencoder framework, both nonlinear projections from and to the observation are considered simultaneously. Additionally, instead of fully connecting used in the conventional autoencoder, the SAGP achieves the mappings utilizing the GP, which remarkably reduces the number of estimated parameters and avoids the phenomenon of overfitting. To make the proposed method adaptive for classification, a discriminative regularization is embedded into the proposed method. In the optimization process, an efficient algorithm based on the alternating direction method and gradient decent techniques is designed to solve the encoder and decoder parts alternatively. Experimental results on three real-world data sets substantiate the effectiveness and superiority of the proposed approach as compared with the state of the art.

关键词： autoencoder discriminative Gaussian process (GP) kernel latent variable model multiview

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