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Reduced-Complexity End-to-End variational autoencoder for on Board Satellite Image Compression

REMOTE SENSING 2021年第3期13卷 447-447页

作者： Alves de Oliveira, Vinicius Chabert, Marie Oberlin, Thomas Poulliat, Charly Bruno, Mickael Latry, Christophe Carlavan, Mikael Henrot, Simon Falzon, Frederic Camarero, Roberto Univ Toulouse IRIT INP ENSEEIHT F-31071 Toulouse France Telecommun Space & Aeronaut TeSA Lab F-31500 Toulouse France Univ Toulouse ISAE SUPAERO F-31055 Toulouse France CNES F-31400 Toulouse France Thales Alenia Space F-06150 Cannes France ESA NL-2201 AZ Noordwijk Netherlands

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off;however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

关键词： remote sensing lossy compression on board compression transform coding rate-distortion JPEG2000 CCSDS learned compression neural networks variational autoencoder complexity

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UNSUPERVISED ADAPTATION FOR HIGH-DIMENSIONAL WITH LIMITED-SAMPLE DATA CLASSIFICATION USING variational autoencoder

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COMPUTING AND INFORMATICS 2021年第1期40卷 1-28页

作者： Mahmud, Mohammad Sultan Huang, Joshua Zhexue Fu, Xianghua Ruby, Rukhsana Wu, Kaishun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis.

关键词： HDLSS problem dimensionality reduction unsupervised framework variational autoencoder deep learning

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Balanced incremental deep reinforcement learning based on variational autoencoder data augmentation for customer credit scoring

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期122卷

作者： Wang, Yadong Jia, Yanlin Zhong, Yu Huang, Jing Xiao, Jin Guangzhou Univ Publ Adm Sch Guangzhou 510006 Guangdong Peoples R China Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China Sichuan Univ Sch Publ Adm Chengdu 610044 Sichuan Peoples R China

Incremental deep reinforcement learning has been successfully applied to different fields in the real-world. However, training deep reinforcement learning models with the data set generated in a certain stage may lead to catastrophic forgetting of trained models, that is, when training models on the new data set, the performance of models will be seriously degraded on the old data set. Therefore, we construct the balanced incremental deep Q-network based on variational autoencoder data augmentation (BIDQN-VADA) model for customer credit scoring. First, the original training set is processed by the random undersampling method to obtain the balanced training set, in which the initial deep Q-network model is trained. Then, the balanced training subset with a fixed number of samples is selected from the original training set, and these samples are augmented by the variational autoencoder data augmentation technology to obtain the balanced augmented training subset. Finally, the balanced training subset and balanced augmented training subset are stored in data stream cache according to the first-in first-out principle for incrementally updating parameters of the deep Q-network model. In order to verify the performance of the model, we conduct a series of experiments on eight real-world customer credit scoring data sets. The experimental results show that the BIDQN-VADA model can achieve the best customer credit scoring performance by combining the balance, incremental and data augmentation process at the same time. More importantly, the performance of the BIDQN-VADA model is significantly better than the other seven classification models.

关键词： Incremental deep reinforcement learning Data augmentation Customer credit scoring variational autoencoder Deep Q-network

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A multimodal dynamical variational autoencoder for audiovisual speech representation learning

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NEURAL NETWORKS 2024年 172卷 106120页

作者： Sadok, Samir Leglaive, Simon Girin, Laurent Alameda-Pineda, Xavier Seguier, Renaud CentraleSupelec UMR CNRS 6164 IETR Gif Sur Yvette France Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab Grenoble France Univ Grenoble Alpes Inria LJK CNRS Grenoble France

High -dimensional data such as natural images or speech signals exhibit some form of regularity, preventing their dimensions from varying independently. This suggests that there exists a lower dimensional latent representation from which the high -dimensional observed data were generated. Uncovering the hidden explanatory features of complex data is the goal of representation learning, and deep latent variable generative models have emerged as promising unsupervised approaches. In particular, the variational autoencoder (VAE) which is equipped with both a generative and an inference model allows for the analysis, transformation, and generation of various types of data. Over the past few years, the VAE has been extended to deal with data that are either multimodal or dynamical (i.e., sequential). In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to unsupervised audiovisual speech representation learning. The latent space is structured to dissociate the latent dynamical factors that are shared between the modalities from those that are specific to each modality. A static latent variable is also introduced to encode the information that is constant over time within an audiovisual speech sequence. The model is trained in an unsupervised manner on an audiovisual emotional speech dataset, in two stages. In the first stage, a vector quantized VAE (VQ-VAE) is learned independently for each modality, without temporal modeling. The second stage consists in learning the MDVAE model on the intermediate representation of the VQ-VAEs before quantization. The disentanglement between static versus dynamical and modality -specific versus modality -common information occurs during this second training stage. Extensive experiments are conducted to investigate how audiovisual speech latent factors are encoded in the latent space of MDVAE. These experiments include manipulating audiovisual speech, audiovisual facial image denoising, and audiovisual speech emotion

关键词： Deep generative modeling Disentangled representation learning variational autoencoder Multimodal and dynamical data Audiovisual speech processing

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A multi-channel fusion variational autoencoder-based RUL prediction approach for multi-sensor systems

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第11期35卷 116129-116129页

作者： Wang, Yuxiao Suo, Chao Zhao, Yuyu Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

Deep learning (DL)-based approaches have demonstrated remarkable performance in predicting the remaining useful life (RUL) of complex systems, which is beneficial for making timely maintenance decisions. However, the majority of these DL methods suffer from a lack of interpretability, and it is difficult to mine the degradation features in the presence of significant measurement noises. To remedy the deficiency, a multi-channel fusion variational autoencoder (MCFVAE)-based approach is proposed. A feature fusion module is designed to capture and fuse the multi-channel features, which facilitates the disclosure of the degradation information from the multi-sensor data. A variational inference module is further introduced to generate the compressive representations and project them into a latent space as an interpretable component, which can display the degradation degree of the multi-sensor systems. A regressor module is finally utilized to establish the relationship between the compressive representations and the RUL. The superior feature fusion and distribution characteristics learning abilities of the MCFVAE contribute to achieving robust and interpretable RUL prediction. The effectiveness and superiority of the proposed method are experimentally validated through a publicly available Commercial modular aero propulsion system simulation dataset and compared with the existing methods.

关键词： multi-sensor systems variational autoencoder interpretable remaining useful life prediction

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A class imbalanced wafer defect classification framework based on variational autoencoder generative adversarial network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第2期34卷 024008-024008页

作者： Wang, Yitian Wei, Yuxiang Wang, Huan Univ Elect Sci & Technol China Glasgow Coll Chengdu 611731 Peoples R China Tsinghua Univ Dept Ind Engn Beijing Peoples R China

Wafer defect classification (WDC) can be crucial to the wafer fabrication process. Engineers can quickly respond to improve the technological process, averting further defects through WDC. However, due to the complex fabrication steps, wafer defects are different in various types. This causes a severe data imbalance problem in WDC. To effectively solve the problem, this study introduces a class imbalanced WDC based on variational autoencoder Generative Adversarial Network (VAE-GAN). This framework consists of VAE-GAN and wafer defect classifier. Among them, VAE-GAN is responsible for creating new samples to solve the imbalance problem while the classifier is responsible for classifying wafer defect patterns. Specifically, VAE-GAN combines the advantage of a variational autoencoder (VAE) and generative adversarial network. VAE networks can produce subtle differences that do not affect the properties of the data when generating new images. At the same time, the proposed discriminator can help us constrain the generated images to be close to real samples and avoid irrational, feature-missing, and ambiguous samples. WM-811 K dataset is utilized to verify the above method. The experimental results validate that the samples generated by VAE-GAN have a significant improvement in the performance of the WDC system.

关键词： wafer map wafer defects classification variational autoencoder generative adversarial network

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Multi-scale spatial-spectral attention network for multispectral image compression based on variational autoencoder

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SIGNAL PROCESSING 2022年 198卷

作者： Kong, Fanqiang Cao, Tongbo Li, Yunsong Li, Dan Hu, Kedi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Based upon the fact that multispectral image compression needs to remove both spatial and spectral redundancy, recent learnt models via end-to-end manners have shown promising performance. However, most of them ignore the characteristics of multispectral image, i.e., the non-stationarity of spectral correlation and the scale-diversity of spatial features. Meanwhile, they directly utilize fully factorized entropy model, rendering compression performance suboptimal. This paper proposes a Multi-Scale SpatialSpectral Attention Network (MSSSA-Net) based on variational autoencoder (VAE). Our MSSSA-Net (1) incorporates a simple neuroscience-based non-local attention module into attention mechanism to capture the tiny features in adjacent pixels and large-scale features in spatial domain simultaneously, (2) proposes a multi-scale spectral attention block to extract non-stationary correlation of adjacent spectra at different scales. We demonstrate that our MSSSA-Net offers the state-of-the-art performance in comparison with classical algorithms, including JPEG20 0 0 and 3D-SPIHT, and recent learnt image compression models, on 7-band and 8-band datasets from Landsat-8 and WorldView-3 satellites, when measured by PSNR, MSSSIM and Mean Spectral Angle. Extensive ablation experiments have verified the effectiveness of each component, and have demonstrated that, for multispectral image compression, Scale-only Hyperprior can make a better trade-off between compression performance and complexity compared with Mean & Scale Hyperprior and Joint Autoregressive model.

关键词： Hierarchical prior model Learnt multispectral image compression Spatial-spectral attention variational autoencoder

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LSTENet: Cement productivity prediction using a self-attention spatio-temporal variational autoencoder

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POWDER TECHNOLOGY 2024年 436卷

作者： Shi, Guangsi Pan, Shirui Zou, Ruiping Monash Univ Fac Engn Dept Chem & Biol Melbourne Australia Griffith Univ Sch Informat & Commun Technol Southport Australia

In the advent of the Industry 4.0 paradigm, intelligent manufacturing has gained prominence with the integration of advanced Artificial Intelligence (AI) technologies aimed at augmenting production efficiency and mitigating operational costs. Particularly, in process industries such as cement manufacturing, the efficacy of production is intrinsically tied to the manifold process elements involved. The underlying complexity is further accentuated by the fact that the production chain is typified by a multi-variate, time -variant, and non-linear system. Such characteristics exacerbate the challenges associated with predicting the quality of final goods, especially considering the intricate physical-chemical reactions that persist even in steady states, which will reflect to the data representation. Additionally, prevailing prediction systems are impeded by their inability to assimilate ancillary spatiotemporal information, rendering their predictive accuracies suboptimal for practical production demands. Addressing these issues, this study introduces an innovative approach for online prediction of final production quality by employing a Spatiotemporal Neural Network. The core attributes of this technique encompass the extraction of latent spatial information and the effective handling of extensive temporal sequences within two main components by a variational autoencoder framework. In the variational autoencoder, it is achieved through the strategic application of learnable convolutional neural layers, supplemented by gated recurrent layers with self -attention. In a novel approach, these two fundamental components are seamlessly integrated within a single end -to -end optimization framework. Empirical evidence derived from a real -world dataset serves to substantiate the superior performance of our proposed methodology in contrast to extant machine learning algorithms. The findings are indicative of the potential that deep learning architectures harbor in addressing

关键词： Production quality prediction variational autoencoder Spatial-temporal modeling Gated neural networks

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Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder

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COMPUTERS & GEOSCIENCES 2021年 152卷 104762-104762页

作者： Lopez-Alvis, Jorge Laloy, Eric Nguyen, Frederic Hermans, Thomas Univ Liege Urban & Environm Engn Appl Geophys Liege Belgium Univ Ghent Dept Geol Ghent Belgium Belgian Nucl Res Ctr Inst Environm Hlth & Safety Engn & Geosyst Anal Mol Belgium

When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e.g. the geological setting) of the subsurface. In such case, inversion may be formulated in a latent space where a lowdimensional parameterization of the patterns is defined and where Markov chain Monte Carlo or gradient-based methods may be applied. However, the generative mapping between the latent and the original (pixel) representations is usually highly nonlinear which may cause some difficulties for inversion, especially for gradientbased methods. In this contribution we review the conceptual framework of inversion with DGMs and propose that this nonlinearity is caused mainly by changes in topology and curvature induced by the generative function. As a result, we identify a conflict between two goals: the accuracy of the generated patterns and the feasibility of gradient-based inversion. In addition, we show how some of the training parameters of a variational autoencoder, which is a particular instance of a DGM, may be chosen so that a tradeoff between these two goals is achieved and acceptable inversion results are obtained with a stochastic gradient-descent scheme. A series of test cases using synthetic models with channel patterns of different complexity and cross-borehole traveltime tomographic data involving both a linear and a nonlinear forward operator show that the proposed method provides useful results and performs better compared to previous approaches using DGMs with gradient-based inversion.

关键词： Deep generative model Geophysical inversion Geological prior information variational autoencoder Stochastic gradient descent Deep learning

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An Improved Semi-supervised variational autoencoder with Gate Mechanism for Text Classification

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年第10期36卷 2253006-2253006页

作者： Ye, Haiming Zhang, Weiwen Nie, Mengna Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

In recent years, semi-supervised learning has been investigated to take full advantages of increasing unlabeled data. Although pretrained deep learning models are successfully adopted on a massive amount of unlabeled data, they may not be applicable in specific domains as the data is limited. In this paper, we propose a model, termed Semi-supervised variational autoencoder (SVAE), which consists of Gated Convolutional Neural Networks (GCNN) as both the encoder and the decoder. Since the canonical VAE suffers from Kullback-Leibler (KL) vanishing problem, we attach a layer named Scalar after Batch Normalization (BN) to scale the output of the BN. We conduct experiments on two domain-specific datasets with a small amount of data. The results show that SVAE outperforms other alternative baselines for language modeling and semi-supervised learning studies. Especially, the results in the language modeling validate the effect of combining BN and Scalar for tackling the KL vanishing problem. Moreover, the visualization of the latent representations verifies the performance of SVAE on less data.

关键词： Semi-supervised learning variational autoencoder text classification gated convolutional neural networks Kullback-Leibler vanishing problem

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