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

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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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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E-VAN : Enhanced variational autoencoder Network for Mitigating Gender Bias in Static Word Embeddings 22

E-VAN : Enhanced Variational AutoEncoder Network for Mitigat...

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Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval

作者： Swati Tyagi Jiaheng Xie Rick Andrews University Of Delaware Lerner College of Business and Economics USA

ISBN: (纸本)9781450397629

Recent research has shown that pre-trained context-independent word embeddings display biases such as racial bias, gender bias, etc. Using a novel, tunable algorithm, this study attempts to mitigate the hidden gender bias in static embeddings. In order to train the model, an enhanced variational autoencoder (E-VAN) is used to learn the latent space of the embedding. Then the latent distributions are used while adaptively resampling and re-weighting the rare/under-represented data. While the word embeddings retain semantic information, E-VAN effectively mitigates unwanted biased gendered associations. Our method E-VAN outperforms previous state-of-the-art methods in both quantitative and human evaluation.

关键词： Discriminator Gender Bias Natural Language Processing Semi-Supervised Learning. variational autoencoder Word Embedding

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Improvement of thermal comfort for underground space: Data enhancement using variational autoencoder

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BUILDING AND ENVIRONMENT 2022年第PartA期207卷 108457-108457页

作者： Qiao, Renlu Li, Xiangyu Gao, Shuo Ma, Xiwen Tongji Univ 1239 Siping Rd Shanghai Peoples R China Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing Peoples R China Univ Oxford 11a Mansfield Rd Oxford OX1 3SZ England Jackson & Ryan Architects 2370 Rice BlvdSte 210 Houston TX 77005 USA

The proportion of buildings occupying underground space has increased with three-dimensional urban development. Thermal comfort is crucial to the design of underground spaces and plays an important role in the optimization of building environment controls. Owing to limitations in recording various practical environmental parameters, it is difficult to access large data and further to establish an accurate forecasting model for the thermal comfort of an underground space. This paper addresses the problem from the perspective of data enhancement. A model for generating underground space data based on a variational autoencoder is proposed. The model maps data of the thermal comfort of an underground space to a highly compressed latent layer space and generates data in an unsupervised manner. The forecasting models were trained using the generated data, resulting in accuracy improvements of 41.34%-45.31%. Hence, the proposed generative model can learn effective real data features. The results also demonstrate that the adjustment of ventilation is more effective than the adjustment of the temperature and relative humidity in improving the thermal comfort of an underground space. The findings of this research will provide better thermal comfort evaluation for the operational management of building environment in underground spaces.

关键词： Underground space Thermal comfort Data augmentation variational autoencoder Forecasting model

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An Application of Geometric Aspects of variational autoencoder Model to Forgery Detection of Scanned Documents 13

An Application of Geometric Aspects of Variational Autoencod...

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13th International Conference on Machine Vision

作者： Janiszewski, Igor Slugin, Dmitry Andreeva, Elena Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia Smart Engines Serv LLC Moscow Russia

ISBN: (纸本)9781510640412

The paper proposes an approach for matching of digitized copies of business documents. This task arises when comparing two versions of the same document - genuine and forgery - to find possible modifications, for example in the banking sector during the conclusion of contracts in paper form to avoid possible fraud. The matching method of two documents based on comparison images of text lines using variational autoencoder (VAE) trained on genuine images and calculation Fisher information metric to find modifications. Experiments were conducted on the public Payslips dataset (in French). The results show the high quality and reliability of finding document forgeries and are compared to the results of the method which applies OCR and image matching.

关键词： Forgery detection documents matching variational autoencoder Fisher information metric

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Application of domain-adaptive convolutional variational autoencoder for stress-state prediction

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KNOWLEDGE-BASED SYSTEMS 2022年 248卷 1页

作者： Lee, Sang Min Park, Sang-Youn Choi, Byoung-Ho Korea Univ Coll Engn Sch Mech Engn Seoul South Korea

Applying data-driven methods such as deep learning in material mechanics is challenging because producing a sufficiently large, labeled dataset is costly resource-wise. This paper outlines a new approach to overcoming this difficulty by transferring knowledge from a source domain of finite-element-analysis data to a target domain of real-world test-specimen images so that a model capable of accurate and robust predictions in both domains may be constructed. To achieve this transfer of knowledge, discrepancy-based unsupervised domain adaptation is adopted into a convolutional variational autoencoder structure. To evaluate the proposed approach, a four-point bending experiment was conducted on 6061 aluminum alloy and 316 stainless steel to produce 550 unlabeled target-domain data images. The same bending situation was analyzed using the finite-element method implemented in the commercial software package ABAQUS to produce 6000 labeled, source-domain data images. The proposed domain-adaptive convolutional variational autoencoder was trained using the maximum mean discrepancy method on the target-and the source-domain data. The predictions using the domain-adapted convolutional variational autoencoder were relatively more accurate than those using the model trained only on the source domain. It is expected that the proposed approach can address the scarcity of labeled data in various applications of material mechanics and provide a base technology for the development of various data-driven approaches.(C) 2022 Elsevier B.V. All rights reserved.

关键词： Unsupervised domain adaptation Stress analysis Four-point bending variational autoencoder Deep learning Convolutional neural network

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Semi-supervised automatic seizure detection using personalized anomaly detecting variational autoencoder with behind-the-ear EEG

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 213卷 106542-106542页

作者： You, Sungmin Cho, Baek Hwan Shon, Young-Min Seo, Dae-Won Kim, In Young Hanyang Univ Dept Biomed Engn Seoul South Korea Sungkyunkwan Univ Med AI Res Ctr Samsung Med Ctr Sch Med Seoul South Korea Sungkyunkwan Univ Samsung Adv Inst Hlth Sci & Technol Dept Med Device Management & Res Seoul South Korea Sungkyunkwan Univ Samsung Med Ctr Dept Neurol Sch Med Seoul South Korea

Background and Objective: Epilepsy is one of the most common neurologic diseases worldwide, and 30% of the patients live with uncontrolled seizures. For the safety of patients with epilepsy, an automatic seizure detection algorithm for continuous seizure monitoring in daily life is important to reduce risks related to seizures, including sudden unexpected death. Previous researchers applied machine learning to detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to identify. Furthermore, the imbalance problem due to the small proportion of ictal events caused poor prediction performance in supervised learning approaches. This study aimed to present a personalized deep learning-based anomaly detection algorithm for seizure monitoring with behind-the-ear electroen-cephalogram (EEG) signals. Methods: We collected behind-the-ear EEG signals from 16 patients with epilepsy in the hospital and used them to develop and evaluate seizure detection algorithms. We modified the variational autoencoder network to learn the latent representation of normal EEG signals and performed seizure detection by measuring the anomalies in EEG signals using the trained network. To personalize the algorithm, we also proposed a method to calibrate the anomaly score for each patient by comparing the representations in the latent space. Results: Our proposed algorithm showed a sensitivity of 90.4% with a false alarm rate of 0.83 per hour without personal calibration. On the other hand, the one-class support vector machine only showed a sensitivity of 84.6% with a false alarm rate of 2.17 per hour. Furthermore, our proposed model with per-sonal calibration achieved 94.2% sensitivity with a false alarm rate of 0.29 while detecting 49 of 52 ictal events. Conclusions: We proposed a novel seizure detection algorithm with behind-the-ear EEG signals via semi-supervised learning of an anomaly detecting variational autoencoder and personalization method of an

关键词： Epilepsy Seizure detection Deep learning Anomaly detection Behind-the-ear EEG variational autoencoder

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Smart anomaly detection for Slocum underwater gliders with a variational autoencoder with long short-term memory networks

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APPLIED OCEAN RESEARCH 2022年 120卷 103030-103030页

作者： Bedja-Johnson, Zachary Wu, Peng Grande, Davide Anderlini, Enrico UCL Dept Mech Engn Gower St London WC1E 6BT England

Autonomous underwater vehicles (AUVs) are used extensively for monitoring the world's oceans, taking measurements of oceanographic characteristics along the water column. Presently, there is no holistic anomaly detection system in operation and AUVs require experienced pilots to monitor the progress of missions. This results in a large operational overhead and reduces the number of AUVs that can be deployed simultaneously. This article proposes an online anomaly detection system for underwater gliders based on a data-driven approach. A novel Long Short-Term Memory (LSTM) variational autoencoder (VAE) has been developed and trained using field data from four deployments with healthy glider behaviour and then tested against four deployments where faults are present. The system is able to detect wing loss with a high degree of accuracy on gliders unseen during the models training, highlighting the generality of the model to different platforms. Additionally, the VAE method outperforms model-based solution for the detection of biofouling, proving its generality to different types of anomalies. The proposed smart anomaly detection will contribute to increasing the capacity of AUVs and reducing the dependence on support vessels and experienced pilots.

关键词： Anomaly detection variational autoencoder Long short-term memory network Underwater glider Marine autonomous system

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Comparison of Improved variational autoencoder Models for Human Face Generation

Comparison of Improved Variational Autoencoder Models for Hu...

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作者： Lijie He School of mathematics and statistics Shandong University

The variational autoencoder(VAE) model has evolved a number of VAE improved models in the past 10 years,including CVAE,WAE,NVAE,*** models have greatly improved the calculation speed of VAE and the resolution of generated *** main goal of this paper is to compare the principles of these different models and the effect of generating *** principle analysis method is mainly used to study the improvement direction of the VAE model in different *** main ideas for improving the VAE model include optimizing the loss function,optimizing the objective function,introducing other parameters,or improving code *** models even add many algorithms in computer vision in for improving image *** the experiments in this paper,results show the image results processed by different *** the current optimal model NVAE,this algorithm has solved most of the VAE image blur problems,and has achieved perfection in image *** improvement of the VAE model in the future may require innovative *** the current principle and model structure,the room for improvement of the VAE model is relatively *** final experiment shows that to improve the quality of VAE generated images,it is required to optimize the objective function,optimize the algorithm and add image visual processing,and then the generated images will be significantly improved.

关键词： variational autoencoder deep learning image generation principle analysis

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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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