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Hyperspectral target detection using self-supervised background learning

ADVANCES IN SPACE RESEARCH 2024年第2期74卷 628-646页

作者： Ali, Muhammad Khizer Amin, Benish Maud, Abdur Rahman Bhatti, Farrukh Aziz Sukhia, Komal Nain Khurshid, Khurram Inst Space Technol Dept Elect Engn iVis Lab Islamabad 44000 Pakistan Bahria Univ Ctr Excellence Artificial Intelligence CoE AI Islamabad 44000 Pakistan Alf Ain Technol Pvt Ltd Lahore 54890 Pakistan NewVat Technol Pvt Ltd Islamabad 44000 Pakistan

Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels' contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold. (c) 2024 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Remote sensing Hyperspectral target detection Self -supervised learning adversarial autoencoder

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Root cause analysis of manufacturing variation from optical scanning data

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ANNALS OF OPERATIONS RESEARCH 2024年第1-2期339卷 111-130页

作者： Bui, Anh Tuan Virginia Commonwealth Univ Dept Stat Sci & Operat Res Richmond VA 23284 USA

Identifying the root causes of part-to-part variation is a central problem in most six-sigma programs, especially of modern manufacturing processes. This is challenging as the sources and patterns of the variation are often unknown or previously unidentified. A small literature aims to address this problem by discovering unknown, previously unidentified variation sources, in a manner that helps understand their nature, from only a sample of measurement data. However, the common solution of this literature is unideal for this objective in terms of both methodology and metrology aspects. This paper proposes a convolutional generative modeling framework for optical scanning data to address this limitation. The proposed approach can correctly discover the true variation sources and visualize their individual patterns in two manufacturing examples, without any prior knowledge of the variation. The approach also outperforms state-of-the-art methods in these examples.

关键词： adversarial autoencoder Convolutional neural networks Generative adversarial networks (GAN) Phase I analysis Statistical process control Variation reduction

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PESTA: An Elastic Motion Capture Data Retrieval Method

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Journal of Computer Science & Technology 2023年第4期38卷 867-884页

作者：蒋子飞李伟黄艳尹义龙郭宗杰彭京亮 School of Software Shandong UniversityJinan 250101China Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles 90089U.S.A Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan 250022China School of Information Science and Engineering University of JinanJinan 250022China

Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data *** clips may not be neatly segmented and labeled,increasing the difficulty of *** order to effectively retrieve such data,we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment(PESTA)in this *** retrieves similarities at the sub-sequence level,achieves robustness against singular frames and enables control of tradeoff between precision and *** firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and,based on which,compactly symbolizes any MoCap ***,it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the ***,it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed *** performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.

关键词： motion capture(MoCap) content-based retrieval adversarial autoencoder temporal alignment

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Deep Mixture of adversarial autoencoders Clustering Network 4th

Deep Mixture of Adversarial Autoencoders Clustering Network

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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Liu, Aofu Ji, Zexuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

ISBN: (纸本)9783030880040;9783030880033

As one of the most fundamental technologies in machine learning, unsupervised clustering methods have been vastly promoted due to the rapid development of deep learning. A widely utilized assumption is that the data is a set of low-dimensional manifolds, thus the key challenge for clustering methods is to ensure the potential features be completely separated. In this paper, we propose a mixture of adversarial autoencoders clustering (MAAE) network to solve the above problem. The data of each cluster is represented by one adversarial autoencoder. By introducing the adversarial information, the aggregated posterior of the hidden code vector of the autoencoder can better match with the prior distribution which is constructed based on the generalized Gaussian mixture model. So the latent features embedded in data can be well separated. The clustering network jointly learns the nonlinear data representation and the collection of adversarial autoencoders. The clustering results are obtained byminimizing the reconstruction loss of the mixture of adversarial autoencoders network. We perform experiments on popular standard datasets, and the results show that the proposed algorithm is effective in both clustering and feature separation.

关键词： Clustering Mixture autoencoders adversarial autoencoder

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A Feature Mapping Technique for Complex Data Object Generation With Likelihood and Deep Generative Approaches

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IEEE ACCESS 2023年 11卷 136643-136653页

作者： Muramudalige, Shashika R. Jayasumana, Anura P. Wang, Haonan Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Stat Ft Collins CO 80523 USA

When a sufficient amount of training data is available, Machine Learning (ML) models show great promise for solving problems involving complex and dynamic patterns. Social and behavioral domains are rich with such challenging problems, with complex object data extracted from documents, surveys, etc., and represented in forms such as graphs and trees. However, many social and behavioral data sets are inherently sparse and incomplete. The same data field may be unavailable in different records of a data set due to different causes, e.g., because it was not measured, not known, or simply not applicable to that particular record. Furthermore, collection challenges, cost, lack of participation, small affected populations, etc., result in very small sets of data. Resulting unconventional datasets cannot be directly used with potent approaches such as machine learning. A technique to model and synthesize large sets of such complex data objects while maintaining the same statistical and topological characteristics of original data helps overcome these challenges. We propose a novel feature mapping technique to eliminate data inconsistencies and model data objects from unconventional datasets. The feature-mapped data objects are used to synthesize data using two likelihood approaches, i.e., multi-variate Gaussian and regular vine copulas, and one generative adversarial approach using an adversarial autoencoder (AAE). We demonstrate the robustness of the proposed technique with three real-world datasets representing disparate domains and validate the performance of likelihood and deep-generative approaches with these object synthesis strategies.

关键词： Data models Behavioral sciences Synthetic data Social factors Social networking (online) Object oriented modeling Generative adversarial networks adversarial autoencoder copulas synthetic data generation generative adversarial networks

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Inductive Conformal Out-of-distribution Detection based on adversarial autoencoders

Inductive Conformal Out-of-distribution Detection based on A...

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IEEE International Conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Cai, Feiyang Ozdagli, Ali, I Potteiger, Nicholas Koutsoukos, Xenofon Vanderbilt Univ Inst Software Integrated Syst Nashville TN 37235 USA

ISBN: (纸本)9781665431569

Machine learning components are used extensively to cope with various complex tasks in highly-uncertain environments. However, Out-Of-Distribution (OOD) data may lead to predictions with large errors and degrade performance considerably. This paper first introduces different types of OOD data and then presents an approach for OOD detection for classification problems efficiently. Our approach utilizes an adversarial autoencoder (AAE) for representing the training distribution and Inductive Conformal Anomaly Detection (ICAD) for online detecting OOD high-dimensional data. Experimental results using several datasets demonstrate that the approach can detect various types of OOD data with a small number of false alarms. Moreover, the execution time is very short, allowing for online detection.

关键词： Out-of-distribution detection Machine learning components for classification adversarial autoencoder Inductive conformal anomaly detection

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EAAE: A Generative adversarial Mechanism Based Classfication Method for Small-scale Datasets

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NEURAL PROCESSING LETTERS 2023年第2期55卷 969-987页

作者： Chen, Ping Deng, Yuhui Zou, Qiang Lu, Lijuan Li, Hong Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China Southwest Univ Dept Comp Sci Chongqing 400715 Peoples R China South China Univ Technol Sch Business Adm Guangzhou 510640 Peoples R China

When used for small-scale datasets classification tasks, deep neural networks are difficult to train, which results in the network not extracting useful features and the low accuracy of network. This paper proposes a method called Enhanced adversarial autoencoders (EAAE) to improve the accuracy of classification in small-scale datasets scenarios. We reuse the decoder of the adversarial autoencoders to generate data for the specified category, which expands the training space of the samples. In order to capture the features of the generated data, we also reuse the encoder to generate the corresponding latent label variables and train them in a supervised manner. To maintain and improve the generative power of the decoder, we impose conditional adversarial training on the generated data. Finally, we use the encoder with latent feature extraction capability to complete the classification by transfer learning. To evaluate the performance of the proposed method, we validate on a real Gastric Cancer dataset and five widely used UCI public datasets. Extensive experiments show that EAAE improves significantly in classification accuracy on small-scale datasets and performs better on large-scale datasets compared to popular classification methods. Furthermore, we evaluate the time cost of EAAE and conduct ablation experiments to demonstrate the effectiveness of the various modules of EAAE.

关键词： Small-scale datasets Deep learning adversarial autoencoder Generative adversarial network Classification tasks

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The effect of augmentation and transfer learning on the modelling of lower-limb sockets using 3D adversarial autoencoders

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DISPLAYS 2022年 74卷

作者： Costa, Ana Rodrigues, Daniel Castro, Marina Assis, Sofia Oliveira, Helder P. Univ Porto Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Adapttech Rua Oliveira Monteiro 649 P-4050445 Porto Portugal INESC TEC Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Fac Sci Rua Campo Alegre P-4169007 Porto Portugal

Lower limb amputation is a condition affecting millions of people worldwide. Patients are often prescribed with lower limb prostheses to aid their mobility, but these prostheses require frequent adjustments through an iterative and manual process, which heavily depends on patient feedback and on the prosthetist's experience. New computer-aided design and manufacturing technologies have been emerging as ways to improve the fitting process by creating virtual models of the prosthesis' interface component with the limb, the socket. Using adversarial autoencoders, a generative model describing both transtibial and transfemoral sockets was created. Two strategies were tested to counteract the small size of the dataset: transfer learning using the ModelNet dataset and data augmentation through a previously validated socket statistical shape model. The minimum reconstruction error was 0.00124 mm and was obtained for the model which combined the two approaches. A single-blind assessment conducted with prosthetists showed that, while generated and real shapes are distinguishable, most generated ones assume plausible shapes. Our results show that the use of transfer learning allowed for a correct training and regularization of the latent space, inducing in the model generative abilities with potential clinical applications.

关键词： 3D generative modelling adversarial autoencoder Lower limb sockets 3D scanning Transfer learning Data augmentation

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Age-Unbiased Facial Emotion Recognition with Regularizing Self-Attention Value Vector 25th

Age-Unbiased Facial Emotion Recognition with Regularizing Se...

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25th International Conference on Intelligent Data Engineering and Automated Learning

作者： Park, Jaeil Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

ISBN: (纸本)9783031777301;9783031777318

Facial emotion recognition (FER) is a challenging task that involves analyzing emotions based on attributes entangled with the face in an image. The FER model inherently possesses age bias because age-related features such as wrinkles and skin texture can create confusion in understanding the emotions of the elderly and decrease accuracy. This paper proposes a FER method that mitigates age bias by applying adversarial regularization to the value vector of self-attention, which is related to age attributes, to minimize information loss. The proposed method focuses on removing biases without affecting the landmarks that make up facial emotions, as key and query vectors in the attention are associated with facial landmarks while value vector is associated with textural features of the image. An age discriminator interrupts age information in value vectors and restores information through the emotion classifier to minimize excessive loss. Experimental results with two FER datasets, RAF-DB and FACES, show that the proposed method yields superior accuracy while reducing bias compared to the state-of-the-art methods.

关键词： Facial emotion recognition bias mitigation self-attention regularization adversarial autoencoder

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Detecting in-vehicle intrusion via semi-supervised learning-based convolutional adversarial autoencoders

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VEHICULAR COMMUNICATIONS 2022年 38卷

作者： Hoang, Thien-Nu Kim, Daehee Soonchunhyang Univ Dept Future Convergence Technol Asan 31538 Chuncheongnam D South Korea

With the development of autonomous vehicle technology, the controller area network (CAN) bus has become the de facto standard for an in-vehicle communication system because of its simplicity and efficiency. However, without any encryption and authentication mechanisms, the in-vehicle network using the CAN protocol is susceptible to a wide range of attacks. Many studies, which are mostly based on machine learning, have proposed installing an intrusion detection system (IDS) for anomaly detection in the CAN bus system. Although machine learning methods have many advantages for IDS, previous models usually require a large amount of labeled data, which results in high time and labor costs. To handle this problem, we propose a novel semi-supervised learning-based convolutional adversarial autoencoder model in this paper. The proposed model combines two popular deep learning models: autoencoder and generative adversarial networks. First, the model is trained with unlabeled data to learn the manifolds of normal and attack patterns. Then, only a small number of labeled samples are used in supervised training. The proposed model can detect various kinds of message injection attacks, such as DoS, fuzzy, and spoofing, as well as unknown attacks. The experimental results show that the proposed model achieves the highest F1 score of 0.9984 and a low error rate of 0.1% with limited labeled data compared to other supervised methods. In addition, we show that the model can meet the real-time requirement by analyzing the model complexity in terms of the number of trainable parameters and inference time. This study successfully reduced the number of model parameters by five times and the inference time by eight times, compared to a state-of-the-art model.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Controller area network Intrusion detection Semi -supervised learning adversarial autoencoder Convolutional neural networks

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