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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Beggel, Laura Pfeiffer, Michael Bischl, Bernd Bosch Ctr Artificial Intelligence Renningen Germany Ludwig Maximilians Univ Munchen Dept Stat Munich Germany

ISBN: (纸本)9783030461508;9783030461492

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds some threshold. Here we analyze a fundamental problem of this approach when the training set is contaminated with a small fraction of outliers. We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance. In order to counteract this effect, an adversarial autoencoder architecture is adapted, which imposes a prior distribution on the latent representation, typically placing anomalies into low likelihood-regions. Utilizing the likelihood model, potential anomalies can be identified and rejected already during training, which results in an anomaly detector that is significantly more robust to the presence of outliers during training.

关键词： Anomaly detection Robust learning adversarial autoencoder

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Self-Attention-Based Multivariate Anomaly Detection for CPS Time Series Data with adversarial autoencoders 41

Self-Attention-Based Multivariate Anomaly Detection for CPS ...

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第41届中国控制会议

作者： Qiwen Li Tijin Yan Huanhuan Yuan Yuanqing Xia School of Mathematics and Statistics Beijing Institute of Technology School of Automation Beijing Institute of Technology School of Astronautics Northwestern Polytechnical University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Data-driven anomaly detection continues to be challenging due to the increased complexity of modern cyber physical systems(CPS s) and their temporal *** detection techniques are widely used through VAE-based frameworks and RNN-based deep learning ***,VAE and its variants impose too much constraint on extracted latent code,and RNN's autoregressive essence indicates the shortage of parallelism and long-term *** tackle the above issues,we propose TransAAE(Transformer-augmented adversarial autoencoder),a novel unsupervised approach for multivariate time series anomaly *** use of the adversarial autoencoder(AAE) architecture loosens the regularization of latent code,and self-attention mechanism is utilized to extract temporal *** experiments show an average F1 score over 0.9 on three public datasets,which significantly outperforms among the baselines.

关键词： adversarial autoencoder Self-Attention Mechanism Cyber Physical Systems Anomaly Detection

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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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Semi-supervised Learning for Human Activity Recognition Using adversarial autoencoders

Semi-supervised Learning for Human Activity Recognition Usin...

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ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM International Symposium on Wearable Computers (ISWC)

作者： Balabka, Dmitrijs Transport & Telecommun Inst Riga Latvia

ISBN: (纸本)9781450368698

SHL recognition challenge 2019 goal is to recognize eight locomotion and transportation (activities) from the inertial sensor data of a smartphone. The dataset contains information from different mobile-phones placement (torso, bag, hips, hand). Participants must provide their predictions based on test data that contains Hand phone sensors information. Only a small amount of Hand phone labeled data exists in the validation data. Train data consists only of torso, bag and hips placed mobile devices. Team DB proposes to apply deep semi-supervised learning. As the base for our model, we have chosen adversarial autoencoder (AAE) and employ Convolutional Networks for feature extraction. We prove that semi-supervised learning gives possibility to utilize test unlabeled data during AAE training with small amount of validation labeled data and achieve high model accuracy for Human Activity Recognition task.

关键词： Semi-supervised learning Human Activity Recognition adversarial autoencoder CNN

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Multi-modal data novelty detection with adversarial autoencoders

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APPLIED SOFT COMPUTING 2024年 165卷

作者： Chen, Zeqiu Zhao, Kaiyi Sun, Ruizhi China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Sci Res Base Integrated Technol Precis Agr Anim Hu Beijing 100083 Peoples R China

Novelty detection is usually defined as the identification of new or abnormal objects (outliers) from the normal ones (inliers), which has wide potential applications including instrument fault, credit card theft warning, and disease diagnosis in the real world. Most of the existing researches focus on the novelty detection for single- modal data, which may fail to provide an accurate and reliable decision because the single-modal data sometimes cannot fully reflect the actual condition of the problem. However, research on the novelty detection from multiple sources is limited. This study developed an end-to-end deep learning architecture of novelty detection for multi-modal data based on adversarial autoencoders (AAE), which requires no input data of the novelty class during the training process. The proposed model consists of three deep networks, which are trained to compete with each other, collaborate to understand the underlying concept in the normal class, and thus classify the testing samples. Among the three networks, two of them work as the novelty detectors, and the other one called the generator will support the two novelty detectors by enhancing the inliers and distorting the outliers. In addition, a pseudo novelty mechanism is developed to expand the groups of adversarial training with the aim to greatly improve the precision and robustness of the whole model. Then the proposed model is employed for the novelty detection based on the multi-modal data that is composed of images, audios, videos, and texts, etc. The experimental results on the PKU FG-XMedia dataset and the MSR-VTT dataset reveal that our proposed method learns the normal class effectively and is superior to the baseline and state-of-the-art methods.

关键词： Novelty detection Multi-modal data adversarial autoencoder Pseudo novelty sample

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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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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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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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Kick: Shift-N-Overlap Cascades of Transposed Convolutional Layer for Better Autoencoding Reconstruction on Remote Sensing Imagery

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IEEE ACCESS 2020年 8卷 107244-107259页

作者： Hong, Seungkyun Song, Sa-Kwang Korea Univ Sci & Technol UST Dept Data & HPC Sci Daejeon 34113 South Korea Korea Inst Sci & Technol Informat KISTI Res Data Sharing Ctr Daejeon 34141 South Korea

A convolutional autoencoder is an essential deep neural model architecture for understanding and predicting large-scale and widespread multi-dimensional information such as remote sensing imagery. To training a convolutional autoencoder, an automatic image reconstruction from input data and evaluation is repeatedly performed to achieve optimal reconstruction performance. Checkerboard artifacts, which are frequently produced on output images and lead to degraded image quality, are a significant issue during image reconstruction using a convolutional autoencoder. To remedy this coarse visual saliency issue during model training, we propose the & x2018;Kick& x2019;deconvolutional layer - a cascaded transposed convolutional layer with pixel shifting and overlapping for checkerboard pattern smoothing. By using pixel-shifted identity convolutional layers, we improved image reconstruction performance using fewer trainable decoder parameters than previously suggested models without losing reconstruction capability. Moreover, our proposed layer can be used with any type of convolutional autoencoder, including typical convolutional autoencoders and adversarial autoencoders. To evaluate an image reconstruction performance of our suggested deconvolutional layer, we used a dataset containing 12 years of geostationary satellite observation data of East Asia.

关键词： Meteorology Tensors Image reconstruction Visualization Decoding Computational modeling Convolution Deconvolutional layer convolutional autoencoder adversarial autoencoder image reconstruction checkerboard artifact satellite observation

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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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