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Scale-Adaptive Asymmetric Sparse variational autoencoder for Point Cloud Compression

IEEE TRANSACTIONS ON BROADCASTING 2024年第3期70卷 884-894页

作者： Chen, Jian Zhu, Yingtao Huang, Wei Lan, Chengdong Zhao, Tiesong Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transmi Fuzhou 350108 Peoples R China Fuzhou Univ Sch Adv Mfg Fuzhou 350108 Peoples R China Fujian Prov Expressway Network Operat Co Ltd Fuzhou 350018 Peoples R China

Learning-based point cloud compression has achieved great success in Rate-Distortion (RD) efficiency. Existing methods usually utilize variational autoencoder (VAE) network, which might lead to poor detail reconstruction and high computational complexity. To address these issues, we propose a Scale-adaptive Asymmetric Sparse variational autoencoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which exploits multi-resolution branches to aggregate multiscale features at encoder, and excludes symmetric feature fusion branches to control the model complexity at decoder. Second, we design a Scale Adaptive Feature Refinement Structure (SAFRS) to adaptively adjust the number of Feature Refinement Modules (FRMs), thereby improving RD performance with an acceptable computational overhead. Third, we implement our framework with AMSC and SAFRS, and train it with an RD loss based on Fine-grained Weighted Binary Cross-Entropy (FWBCE) function. Experimental results on 8iVFB, Owlii, and MVUV datasets show that our method outperforms several popular methods, with a 90.0% time reduction and a 51.8% BD-BR saving compared with V-PCC. The code will be available soon at https://***/fancj2017/SAS-VAE.

关键词： Point cloud compression Feature extraction Decoding Convolution Three-dimensional displays Octrees Rate-distortion variational autoencoder asymmetric multiscale sparse convolution scale adaptive feature refinement structure

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Anomaly-Based Intrusion Detection From Network Flow Features Using variational autoencoder

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IEEE ACCESS 2020年 8卷 108346-108358页

作者： Zavrak, Sultan Iskefiyeli, Murat Sakarya Univ Dept Comp & Informat Engn TR-54187 Sakarya Turkey Duzce Univ Dept Comp Engn TR-81620 Duzce Turkey Sakarya Univ Dept Comp Engn TR-54187 Sakarya Turkey

The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, autoencoder and variational autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that variational autoencoder performs, for the most part, better than autoencoder and One-Class Support Vector Machine.

关键词： Intrusion detection Feature extraction Telecommunication traffic Deep learning Support vector machines Anomaly detection Computer hacking Flow anomaly detection intrusion detection deep learning variational autoencoder semi-supervised learning

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Banner layout retargeting with hierarchical reinforcement learning and variational autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第24期81卷 34417-34438页

作者： Hu, Hao Zhang, Chao Liang, Yanxue Westlake Univ Hangzhou Peoples R China Westlake Univ Intelligent Ind Res Inst Hangzhou Peoples R China

In many advertising areas, banners are often generated with different display sizes, so designers have to make huge efforts to retarget their designs to each size. Automating such retargeting process can greatly save time for designers and let them put creativity on new ads. This paper proposes a hierarchical reinforcement learning-based (HRL-based) method and a variational autoencoder-based (VAE-based) method by treating the automated banner retargeting problem as a layout retargeting task. The HRL and VAE models are trained separately to learn the scaling and positioning policy of the design elements from an original (base) layout. Hence, the proposed method can generate appropriate layouts for different target banner sizes. Meanwhile, evaluation metrics are proposed to assess the quality of generated layouts and are also reward conditions during the training process. To evaluate performances of the two models, SOTA methods such as Non-linear Inverse Optimization (NIO), Triangle Interpolation (TI), and Layout GAN (LGAN) are implemented and compared. Experimental results show that both HRL- and VAE-based methods retarget design layouts effectively, and the VAE model achieves better performance than the HRL model.

关键词： Layout retargeting Hierarchical reinforcement learning variational autoencoder SOTA methods

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Predicting the quality of a machined workpiece with a variational autoencoder approach

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 719-737页

作者： Proteau, Antoine Tahan, Antoine Zemouri, Ryad Thomas, Marc Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada HESAM Univ CEDRIC Lab Conservatoire Natl Arts & Metiers CNAM F-750141 Paris 03 France

In this article, it is shown that a machine learning approach based only on data from sensors (vibration and current consumption) can be used to predict the geometric dimensioning and tolerancing quality measurement values of machined workpieces in an industrial context. First, a methodology based on a variational autoencoder approach is used, and then a metric based on the concept of Euclidean distance and the 2D latent space produced by the variational autoencoder is proposed. The proposed variational autoencoder regression model is shown capable of predicting the quality measurement values, with a mean square error of 5.2573 x 10(-4) mm. The proposed measurement system also displays a confidence interval of +/- 0.05 mm. Moreover, the resulting 2D latent space is capable of distributing and structuring data based on the quality level and of providing a quick visual support. Compared to the t-SNE method, this latent space displays a better structure. Furthermore, the proposed Euclidean distance metric is correlated to the quality level in both the predicted and observed subsets. This work is also based on an industrial dataset, thus increasing its potential for technological transfer;that in turn allows a better monitoring of the machining process, as well as the prediction of the workpiece quality.

关键词： variational autoencoder Geometric dimensioning and tolerancing 2D-visualization Prognostic Machining process

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Multi-Modal Domain Adaptation variational autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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Adversarial variational autoencoder for attributed graph embedding with high-frequency noise filtering

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APPLIED INTELLIGENCE 2023年第22期53卷 26750-26762页

作者： Zhou, Hui Liu, Xin Li, Xiangju Zhao, Zhongying Li, Chao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Attributed graph embedding aims to learn low-dimensional representations by fully exploiting both topological structure and rich attributes. The embeddings contain more useful information, thus yielding better performance in subsequent analysis tasks. Therefore, attributed network embedding has become an important research direction in recent years. However, most existing algorithms ignore the high-frequency noise contained in the features and do not normalize the embedding, which limits their performance in downstream tasks. To solve the above problem, we design an Adversarial Attributed Graph Embedding algorithm (AAGE) in conjunction with a Laplacian smoothing filter. In particular, the AAGE employs a variational autoencoder (VAE) to learn the embedding of nodes and attributes. Unlike most variational autoencoder-based algorithms, we first apply a carefully-designed Laplacian filter to smooth the features before performing autoencoder, secondly normalize the obtained embeddings of nodes and attributes during the autoencoder process, and finally devise a generative adversarial training method to enhance the robustness of the learned representations. To verify the potential of the proposed AAGE, we evaluate its performance on the tasks of node classification and node clustering on three real-world attributed networks. The results show that AAGE significantly outperforms state-of-the-art methods.

关键词： Attributed network variational autoencoder Normalization Generative adversarial network

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Speech Source Separation Using variational autoencoder and Bandpass Filter

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IEEE ACCESS 2020年 8卷 156219-156231页

作者： Do, Hao Duc Tran, Son Thai Chau, Duc Thanh Univ Sci Fac Informat Technol Ho Chi Minh City 70000 Vietnam Viet Nam Natl Univ Ho Chi Minh City 70000 Vietnam Univ Sci Off Educ & Training Ho Chi Minh City 70000 Vietnam OLLI Technol JSC AI Lab Ho Chi Minh City 70000 Vietnam

Speech source separation is essential for speech-related applications because this process enhances the input speech signal for the main processing model. Most of the current approaches for this task focus on separating the speech of commonly high-frequency noises or a particular background sound. They cannot clear the signals which intersect with the human speech in its frequency range. To deal with this problem, we propose a hybrid approach combining a variational autoencoder (VAE) and a bandpass filter (BPF). This method can extract and enhance the speech signal in the mixture of many elements such as speech signal, the high-frequency noises, and many kinds of different background sounds which interfere with the speech sound. Experimental results showed that our model can extract effectively the speech signal with 15.02 dB in Signal to Interference Ratio (SIR) and 12.99 dB in Signal to Distortion Ratio (SDR). On the other hand, we can adjust the passband to identify the range of frequency at the output signal to apply for a particular application like gender classification.

关键词： Speech enhancement Blind source separation Band-pass filters Music Multiple signal classification Generative model variational autoencoder bandpass filter speech separation

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A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on variational autoencoder

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第4期19卷 2049-2059页

作者： Ji, Cunmei Wang, Yutian Gao, Zhen Li, Lei Ni, Jiancheng Zheng, Chunhou Qufu Normal Univ Sch Software Qufu 273165 Shandong Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical role in many biological processes, such as cell growth, development, differentiation and aging. Increasing studies have revealed that miRNAs are closely involved in many human diseases. Therefore, the prediction of miRNA-disease associations is of great significance to the study of the pathogenesis, diagnosis and intervention of human disease. However, biological experimentally methods are usually expensive in time and money, while computational methods can provide an efficient way to infer the underlying disease-related miRNAs. In this study, we propose a novel method to predict potential miRNA-disease associations, called SVAEMDA. Our method mainly consider the miRNA-disease association prediction as semi-supervised learning problem. SVAEMDA integrates disease semantic similarity, miRNA functional similarity and respective Gaussian interaction profile (GIP) similarities. The integrated similarities are used to learn the representations of diseases and miRNAs. SVAEMDA trains a variational autoencoder based predictor by using known miRNA-disease associations, with the form of concatenated dense vectors. Reconstruction probability of the predictor is used to measure the correlation of the miRNA-disease pairs. Experimental results show that SVAEMDA outperforms other stat-of-the-art methods. AUC values of SVAEMDA of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV) are 0.9464 and 0.9428 respectively. In addition, case studies of three common human diseases indicate that SVAEMDA obtains 100 percent of the top 50 predicted candidates in the benchmark databases. Therefore, SVAEMDA can efficiently and accurately predict the potential associations between diseases and miRNAs.

关键词： Diseases Feature extraction Predictive models Semantics Databases Support vector machines Computational modeling Semi-supervised learning representation learning microRNA-disease association variational autoencoder

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Facial landmark disentangled network with variational autoencoder

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Applied Mathematics(A Journal of Chinese Universities) 2022年第2期37卷 290-305页

作者： LIANG Sen ZHOU Zhi-ze GUO Yu-dong GAO Xuan ZHANG Ju-yong BAO Hu-jun CAD&CG State Labs Zhejiang UniversityHangzhou 310027China School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026China.

Learning disentangled representation of data is a key problem in deep ***,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of face reconstruction,face reenactment and talking head et al..However,due to the sparsity of landmarks and the lack of accurate labels for the factors,it is hard to learn the disentangled representation of *** address these problem,we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations,which is based on a variational autoencoder ***,we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training ***,we implement an identity preservation loss to further enhance the representation ability of identity *** the best of our knowledge,this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.

关键词： disentanglement representation deep learning facial landmarks variational autoencoder

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Network Embedding via Community Based variational autoencoder

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IEEE ACCESS 2019年 7卷 25323-25333页

作者： Shi, Wei Huang, Ling Wang, Chang-Dong Li, Juan-Hui Tang, Yong Fu, Chengzhou Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China

In recent years, network embedding has attracted more and more attention due to its effectiveness and convenience to compress the network structured data. In this paper, we propose a community-based variational autoencoder (ComVAE) model to learn network embedding, which consists of a community detection module and a deep learning module. In the proposed model, both community information and deep learning techniques are utilized to learn low-dimensional vertex representations. First, community information reveals an implicit relationship between vertices from a global view, which can be a supplement to local information and help to improve the embedding quality. To obtain the community information, community detection algorithms are utilized as a module and the modularization design makes the model more flexible. Second, deep learning techniques can not only integrate and preserve the information from both local and global views efficiently but also strengthen the robustness of vertex representations. To demonstrate the performance of our model, extensive experiments are conducted in four downstream tasks, namely, network reconstruction, node classification, link prediction, and visualization. The experimental results show that our model outperforms the state-of-the-art approaches to real-world datasets.

关键词： Network embedding community detection variational autoencoder

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