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8th International Conference on Advanced Cloud and Big Data (CBD)

作者： Sun, Jingyu Wei, Dong Shagor, Md Masum Billa Taiyuan Univ Technol Coll Software Taiyuan Peoples R China Taiyuan Univ Technol Coll Informat & Comp Taiyuan Peoples R China

ISBN: (纸本)9781665423137

Commonly used datasets in recommendation research suffer from unbalanced data distribution, sparsity, and different user rating preferences. All these problems affect the quality of recommendation. Thus, this paper proposed a recommendation model by combining hard example mining with adversarial autoencoder. Considering the difference in users' preference, Mean Model based triplet loss algorithm was introduced to classify the dataset into positive and negative samples and thus improve the quality of the training data. Using classified samples, the rating prediction model was trained from both reconstruction and adversarial aspects. Adam optimization algorithm was used to calculate different update gradients for different parameters. Experimental results show that the recommendation model improves the recommendation accuracy significantly, and several performance indicators are better than baseline models.

关键词： recommendation system hard example mining Mean Model adversarial autoencoder

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Anomaly Detection Using Convolutional adversarial autoencoder and One-class SVM for Landslide Area Detection from Synthetic Aperture Radar Images

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2021年第2期8卷 139-144页

作者： Mabu, Shingo Hirata, Soichiro Kuremoto, Takashi Yamaguchi Univ Grad Sch Sci & Technol Innovat 2-16-1 Tokiwadai Ube Yamaguchi 7558611 Japan Nippon Inst Technol Dept Informat Technol & Media Design 1 Gakuendai Miyashiro Saitama 3458501 Japan

An anomaly detection model using deep learning for detecting disaster-stricken (landslide) areas in synthetic aperture radar images is proposed. Since it is difficult to obtain a large number of training images, especially disaster area images, with annotations, we design an anomaly detection model that only uses normal area images for the training, where the proposed model combines a convolutional adversarial autoencoder, principal component analysis, and one-class support vector machine. In the experiments, the ability in detecting normal and abnormal areas is evaluated. (C) 2021 The Authors. Published by Atlantis Press International B.V.

关键词： Anomaly detection adversarial autoencoder one-class SVM synthetic aperture radar

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DUAL adversarial autoencoder FOR DERMOSCOPIC IMAGE GENERATIVE MODELING 16

DUAL ADVERSARIAL AUTOENCODER FOR DERMOSCOPIC IMAGE GENERATIV...

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16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Yang, Hao-Yu Staib, Lawrence H. Yale Univ New Haven CT 06520 USA CuraCloud Corp Seattle WA 98104 USA

ISBN: (纸本)9781538636411

Skin cancer is a severe public health issue in the United States and worldwide, While Computer Aided Diagnosis (CAD) of dermoscopic images shows potential in accelerating diagnosis and improving accuracy, numerous issues remain that may be addressed by generative modeling. Major challenges in automated skin lesion classification include manual efforts required to label new training data and a relatively limited amount of data compared to more generalized computer vision tasks. We propose a novel generative model based on a dual discrimination training algorithm for autoencoders. At each training iteration, the encoder and decoder undergo two stages of adversarial training by two individual discriminator networks, The algorithm is end-to-end trainable with standard hack-propagation. In contrast with traditional autoencoders, our method incorporates extra constraints via adversarial training, which results in visually realistic synthetic data, We demonstrate the versatility of the proposed method and applications on numerous tasks including latent space visualization, data augmentation, and image denoising.

关键词： adversarial autoencoder Unsupervised learning Dermoscopy Skin lesion

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Anomaly detection of solder joint on print circuit board by using adversarial autoencoder 14

Anomaly detection of solder joint on print circuit board by ...

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14th International Conference on Quality Control by Artificial Vision (QCAV)

作者： Goto, Keisuke Kato, Kunihito Nakatsuka, Shunsuke Saito, Takaho Aizawa, Hiroaki Gifu Univ Fac Engn Gifu Japan Aisin AW CO LTD Kariya Aichi Japan

ISBN: (数字)9781510630543

ISBN: (纸本)9781510630543

We propose a defect detection method of solders on a printed circuit board using X-ray CT inspection system and adversarial autoencoder (AAE)([1]). We obtain sliced images of the solder using X-ray CT and extract their features that follow the standard normal distribution by using AAE. Then, the solder defects are detected by Hotelling's T square([2]). As a result of experiments, we show that we can classify normal and anomalous data samples completely on the condition of training with large normal samples and small anomalous samples.

关键词： X-ray-CT adversarial autoencoder Hotelling's T square Anomaly Detection

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An Optimized Self-representation Subspace Clustering Model Based on adversarial autoencoder 20th

An Optimized Self-representation Subspace Clustering Model B...

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20th International Conference on Intelligent Computing (ICIC)

作者： Sun, Deyan Chen, Dufeng Liu, Hai Chen, Wei Liu, Jueting Yang, Yuancan Wang, Zehua Zhu, Pengcheng China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Beijing Geotech & Invest Engn Inst Beijing 100080 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Emergency Management Big Data Ctr Beijing 100010 Peoples R China

ISBN: (纸本)9789819756742;9789819756759

Subspace clustering, as a high-dimensional data clustering method has been widely applied in multiple fields such as data mining and computer vision. The existing deep subspace clustering methods only consider the distribution characteristics of low dimensional representations of data, and do not effectively utilize the implicit spatial distribution of data categories to improve clustering performance. This paper proposes a self-representation subspace clustering model based on adversarial autoencoder (AAESC). In this model, the low dimension representation of data is subject to Normal distribution, and the class representation is subject to polynomial distribution by antagonizing the low dimension representation and class representation of self codec learning data, and using two antagonizing generation networks respectively. Then, by introducing from the presentation layer, the data similarity matrix is constructed based on the low dimensional representation of the data, and the spectral clustering algorithm is used to obtain the clustering results. Experiments have shown that the self-representation subspace clustering model based on adversarial self-coding can better learn the low dimensional representation of data and construct a good data similarity matrix based on it, with better clustering performance than other methods.

关键词： Subspace Clustering adversarial autoencoder Self-representation Neural Network

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Damage Detection and Localization for Indirect Bridge Monitoring Exploiting adversarial autoencoder and Wavelet Transform 10th

Damage Detection and Localization for Indirect Bridge Monito...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Kaur, Kirandeep Alamdari, Mehri Makki Chang, Kai Chun Kim, Chul Woo Atroshchenko, Elena Univ New South Wales Sch Civil & Environm Engn Ctr Infrastruct Engn & Safety Sydney NSW 2052 Australia Kyoto Univ Grad Sch Engn Dept Civil & Earth Resources Engn Kyoto 6158540 Japan

ISBN: (纸本)9783031072543;9783031072536

Indirect bridge monitoring provides an economical procedure to ensure reliable operation of the bridges by utilizing the vehicle responses passing over the bridge. The present work proposes a unified framework for damage detection, assessment, and localization using adversarial autoencoder and wavelet transform in an unsupervised manner. Vehicle acceleration signals from healthy bridge state only are considered for adversarial autoencoder model training. Estimated reconstruction error is used as a damage detection index. Pre-processing techniques of spectrum filtering, and signal averaging are adopted to improve the model performance. Further, the locations of the detected damages are estimated by employing wavelet transform along with signal pre-processing techniques. The approach is tested on numerically simulated vehicle acceleration signals which are obtained by considering a half-car model on a simply supported bridge using finite element analysis. Different damage scenarios with varying severity at different bridge locations have been considered. It has been investigated that the proposed approach is successful in detecting damages with varying damage severity alongside locating their respective locations. The proposed approach also outperforms other unsupervised learning techniques by detecting and isolating different damages effectively.

关键词： adversarial autoencoder Indirect monitoring Moving load Deep learning Wavelet transform

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AAANE: Attention-Based adversarial autoencoder for Multi-scale Network Embedding 23rd

AAANE: Attention-Based Adversarial Autoencoder for Multi-sca...

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23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Sang, Lei Xu, Min Qian, Shengsheng Wu, Xindong Hefei Univ Technol Sch Comp Sci & Informat Technol Hefei Peoples R China Univ Technol Sydney Fac Engn & IT Ultimo Australia Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9783030161422;9783030161415

Network embedding represents nodes in a continuous vector space and preserves structure information from a network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. In this paper, we propose an Attention-based Adver-sarial autoencoder Network Embedding (AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: (1) an attention-based autoencoder that effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training, and (2) an adversarial regularization guides the autoencoder in learning robust representations by matching the posterior distribution of the latent embeddings to a given prior distribution. Experimental results on real-world networks show that the proposed approach outperforms strong baselines.

关键词： Network embedding Multi-scale Attention adversarial autoencoder

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Image-Based Process Monitoring via Generative adversarial autoencoder with Applications to Rolling Defect Detection

Image-Based Process Monitoring via Generative Adversarial Au...

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作者： Yeh, Huai-Ming Arizona State University

学位级别：M.S.

Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.

关键词： Industrial engineering Information technology Computer science adversarial autoencoder anomaly detection generative adversarial networks machine learning statistic unsupervised learning

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Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder

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JOURNAL OF SOUND AND VIBRATION 2023年第1期550卷

作者： Hurtado, A. Calderon Kaur, K. Alamdari, M. Makki Atroshchenko, E. Chang, K. C. Kim, C. W. Univ New South Wales Ctr Infrastructure Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia Kyoto Univ Grad Sch Engn Dept Civil & Earth Resources Engn Kyoto 6158540 Japan

This paper studies the problem of bridge health monitoring in an unsupervised manner utilizing only the measured responses from a vehicle passing over a bridge. A half-car model along with a simply supported beam is adopted for numerically simulating vehicle- bridge interaction (VBI). Multiple bridge states including healthy bridge and damaged bridge with varying severity at different locations are considered. A data-driven approach based on adversarial autoencoder (AAE) by incorporating the generative capabilities of adversarial autoencoder and pre-processing techniques including frequency filtering and signal averaging is proposed. Vehicle acceleration responses associated only with the healthy bridge state are used for model training. Reconstruction error estimated by the proposed model is adopted as a damage detection index. Along with the detection of the damage in the bridge, the proposed framework is also able to estimate the severity of the damage. The proposed framework also overcomes the limitations of other unsupervised learning approaches such as principal component analysis and autoencoders due to its better representation of data in the latent sub-space with an additional prior distribution constraint. Further, the proposed framework is validated through experimentally obtained data from a laboratory scale bridge model. The contribution of this work is three-fold: First, an adversarial autoencoder-based unsupervised learning framework supplemented by appropriate pre-processing techniques is proposed for drive-by bridge monitoring for the first time, and its implementation is extensively investigated. Second, the superior performance of the proposed AAE framework compared to the competing techniques is demonstrated. Finally, this paper presents one of the early successful attempts of drive-by bridge inspection for monitoring the progressive change in the structure of a bridge. Research presented in this work can potentially open up new opportunities for

关键词： adversarial autoencoder Deep learning Indirect SHM Bridge monitoring Damage

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Detecting collusive spammers on e-commerce websites based on reinforcement learning and adversarial autoencoder

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Zhang, Fuzhi Yuan, Shuai Wu, Jiayi Zhang, Peng Chao, Jinbo Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Province Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebei Qinhuangdao Hebei Province Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

The collusive spamming behavior on e-commerce websites seriously affects the purchase decisions of consumers and disrupts the fair competition order among merchants. To address this problem, many approaches have been proposed to detect collusive spammers on e-commerce websites. However, the existing approaches rely on handcrafted features for detecting collusive spammers, which is costly and time-consuming. With this limitation in mind, we propose a collusive spammer detection approach based on reinforcement learning and adversarial autoencoder. First, we model the given dataset as a user-product bipartite graph that is treated as the agent's interactive environment and use the modified reinforcement learning algorithm to obtain candidate groups. Second, we exploit the Doc2Vec model to generate the embedding of each candidate group and devise an adversarial autoencoder-based one-class classification model for detecting collusive spammers. The experimental results on the real-world review datasets show that the proposed approach has better detection performance than the state-of-the-art methods.

关键词： Collusive spammers Collusive spammer detection Reinforcement learning adversarial autoencoder

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