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Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection

OPTICS AND LASERS IN ENGINEERING 2021年 136卷 106324-106324页

作者： Liu, Jie Song, Kechen Feng, Mingzheng Yan, Yunhui Tu, Zhibiao Zhu, Liu Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China Northeastern Univ Minist Educ China Key Lab Vibrat & Control Aeroprop Syst Shenyang Liaoning Peoples R China Taizhou Univ Sch Pharmaceut & Mat Engn Taizhou Zhejiang Peoples R China

Anomaly detection in the automated optical quality inspection is of great important for guaranteeing the surface quality of industrial products. Most related methods are based on supervised learning techniques, which require a large number of normal and anomalous samples to obtain a robust classifier. However, the diversity of potential defects and low availability of defective samples during manufacturing bring more challenges to anomaly detection. Based on the encoder-decoder-encoder paradigm, a semi-supervised anomaly detection method Dual Prototype auto-encoder (DPAE) is proposed in this paper. At the training stage, the dual prototype loss and reconstruction loss are introduced to encourage the latent vectors generated by the encoders to keep closer to their own prototype. Therefore, two latent vectors of the normal image tend to be closer, and large distance between the latent vectors indicates an anomaly. And we also construct the Aluminum Profile Surface Defect (APSD) dataset for the anomaly detection task. Finally, extensive experiments on four datasets show that DPAE is effective and outperforms state-of-the-art methods.

关键词： Anomaly detection auto-encoder Surface defect Semi-supervised learning

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New Results in End-to-end Image and Video Compression by Deep Learning 28

New Results in End-to-end Image and Video Compression by Dee...

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Ozsoy, Gokberk Yilmaz, Melih Kirmemis, Ogun Tekalp, A. Murat Koc Univ Elekt & Elekt Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781728172064

Expanding ubiquity of high-resolution digital video over the Internet calls for better compression methods to enable streaming with higher compression efficiency and lower latency. Recently, important gains have been achieved in learned image compression by using end-to-end learned models. However, these improvements haven't been fully leveraged in video compression. This paper aims to improve upon work proposed by Lu et al. in CVPR 2019, which has been claimed to outperform conventional video codecs in terms of PSNR and provide some implementation details that are absent in the original paper. Ultimately, we show that modeling latent symbols by Laplacian distribution outperforms the Gaussian assumption used in the original work and also demonstrate in a repeatable fashion that our learned model is superior to x264 video codec in terms of PSNR over a range of compression rates measured by bit-per-pixel.

关键词： Learned video compression deep neural network auto-encoder end-to-end optimization Laplacian distribution

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A Time-domain Unsupervised Learning Based Sound Source Localization Method 3

A Time-domain Unsupervised Learning Based Sound Source Local...

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3rd IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Huang, Yankun Wu, Xihong Qu, Tianshu Peking Univ Speech & Hearing Res Ctr Beijing Peoples R China

ISBN: (纸本)9781728188232

In recent years, deep neural networks have been applied in many fields. In this paper, a time-domain unsupervised learning based sound source localization method is proposed, where auto-encoder neural networks are adopted so that some operation like time-delay compensation can be removed and there is no need to prepare training data with precise alignment labels. In order to improve its performance, a training strategy based on the multi-task learning and acoustic transfer function is proposed as well, called joint training of alternating and splitting. Experiments show that the proposed method can learn the transmission characteristics, including the change of time delay and intensity. What's more, the proposed method also has better performance compared with SRP-PHAT, MUSIC and two other neural networks based methods.

关键词： Sound source localization unsupervised learning auto-encoder direction of arrival multi-task learning

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Identification of Cancer Mediating Biomarkers using Stacked Denoising autoencoder Model - An Application on Human Lung Data

Identification of Cancer Mediating Biomarkers using Stacked ...

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International Conference on Computational Intelligence and Data Science (ICCIDS)

作者： Sheet, Sougata Ghosh, Anupam Ghosh, Ranjan Chakrabarti, Amlan Univ Calcutta AK Choudhury Sch Informat Technol Kolkata 700106 India Netaji Subhash Engn Coll Dept Comp Sci & Engn Kolkata 700152 India

In this work, we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma. At first we have trained the data for feature selection by using Stacked Denoising auto-encoder (SDAE) and for backpropagation we have used Multilayer Perceptron (MLP) procedure. We said this model is MLP-SDAE. The process include classification of genes according to correlation coefficient value and select few feasible genes. The superiority of the method has been established some present gene selection procedures like Support Vector Machine (SVM), Significance Analysis of Microarry (SAM), Bayesian Regularization (BR), Neighborhood Analysis (NA), and Gaussian Mixture Model (GMM). The MLP-SDAE model has been effectively used to one human lung microarray gene expression data. The result are appropriately verify by preliminary analysis, t-test, and gene expression profile plots. In this method, we have established more number of true positive genes then another present procedures. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： auto-encoder Deep neural network Gene expression Multilayer perceptron p-value t-test

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An autoencoder Based Approach to Simulate Sports Games 7th

An Autoencoder Based Approach to Simulate Sports Games

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7th International Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA)

作者： Vaswani, Ashwin Ganguly, Rijul Shah, Het Ranjit, Sharan S. Pandit, Shrey Bothara, Samruddhi Birla Inst Technol & Sci Pilani KK Birla Goa Campus Sancoale Goa India

ISBN: (纸本)9783030649111;9783030649128

Sports data has become widely available in the recent past. With the improvement of machine learning techniques, there have been attempts to use sports data to analyze not only the outcome of individual games but also to improve insights and strategies. The outbreak of COVID-19 has interrupted sports leagues globally, giving rise to increasing questions and speculations about the outcome of this season's leagues. What if the season was not interrupted and concluded normally? Which teams would end up winning trophies? Which players would perform the best? Which team would end their season on a high and which teams would fail to keep up with the pressure? We aim to tackle this problem and develop a solution. In this paper, we propose UCLData, which is a dataset containing detailed information of UEFA Champions League games played over the past six years. We also propose a novel autoencoder based machine learning pipeline that can come up with a story on how the rest of the season will pan out.

关键词： Sports analytics Machine learning Data mining auto-encoder

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LOSSY GEOMETRY COMPRESSION OF 3D POINT CLOUD DATA VIA AN ADAPTIVE OCTREE-GUIDED NETWORK

LOSSY GEOMETRY COMPRESSION OF 3D POINT CLOUD DATA VIA AN ADA...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wu, Xuanzheng Wang, Xu Hou, Junhui Ma, Lin Zhou, Yu Jiang, Jianmin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Guangdong Lab Artif Intelligence & Digital Econ S Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China

ISBN: (纸本)9781728113319

In this paper, we propose a deep learning based framework for point cloud geometry lossy compression via hybrid representation of point cloud. First, the input raw 3D point cloud data is adaptively decomposed into non-overlapping local patches through adaptive Octree decomposition and clustering. Second, a framework of point cloud auto-encoder network with quantization layer is proposed for learning compact latent feature representation from each patch. Specifically, the proposed point cloud auto-encoder networks with different input size are trained for achieving optimal rate-distortion (RD) performance. Final, bitstream specifications of proposed compression systems with additional signaled meta-data and header information are designed to support parallel decoding and successive reconstruction. Experimental results shows that our proposed method can achieve 40.20% bitrate saving in average than the existing standard Geometry based Point Cloud Compression (G-PCC) codec.

关键词： 3D point cloud auto-encoder entropy estimation geometry compression

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Unsupervised Feature Selection using encoder-Decoder Networks 6

Unsupervised Feature Selection using Encoder-Decoder Network...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： SharifiPour, Sasan Fayyazi, Hossein Sabokro, Mohammad Med Fanavaran Plus Co Res & Dev Dept Karaj Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

ISBN: (纸本)9781728186290

Feature selection is one of the most important techniques for dimension reduction in a wide range of tasks. This paper presents a simple yet efficient method for unsupervised feature selection. To learn an encoder-decoder network on a huge number of training samples, the initial weights of such network must be regularly updated until the convergence reached. We interestingly investigated that the weights of an input neuron to the latent space neurons are highly correlated with the importance of them. We tailor an efficient measure as the importance of each feature value relies on the changing of connected weights to it. The experimental results confirm that the performance of proposed method is comparable, even better than other state-of-the-art methods.

关键词： feature selection auto-encoder unsupervised

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Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2021年 187卷 103111-103111页

作者： Lee, Sang-Woong Sidqi, Haval Mohammed Mohammadi, Mokhtar Rashidi, Shima Rahmani, Amir Masoud Masdari, Mohammad Hosseinzadeh, Mehdi Gachon Univ Pattern Recognit & Machine Learning Lab 1342 Seongnamdaero Seongnam 13120 South Korea Sulaimani Polytech Univ Dept Database Coll Informat Sulaymaniyah Iraq Lebanese French Univ Dept Informat Technol Erbil Kurdistan Regio Iraq Univ Human Dev Dept Comp Sci Sulaymaniyah Iraq Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Yunlin Taiwan Khazar Univ Dept Comp Sci Baku Azerbaijan Islamic Azad Univ Urmia Branch Dept Comp Sci Orumiyeh Iran

Providing a high-performance Intrusion Detection System (IDS) can be very effective in controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative impacts of the security attacks on computer systems and networks, various Artificial Intelligence (AI)-based techniques have been used to introduce versatile IDS approaches. Deep learning is a branch of AI techniques, mainly based on multi-layer artificial neural networks. Recently, deep learning techniques have gained momentum in the intrusion detection domain and several IDS approaches are provided in the literature using various deep neural networks to deal with privacy concerns and security threats. For this purpose, this article focuses on the deep IDS approaches and investigates how deep learning networks are employed by different approaches in different steps of the intrusion detection process to achieve better results. It classifies the studied IDS schemes regarding the deep learning networks utilized in them and describes their main contributions and capabilities. Besides, in each category, their main features such as evaluated metrics, datasets, simulators, and environments are compared. Also, a comparison of the deep IDS approaches main properties are provided to illuminate the main techniques applied in them as well as the area less focused in the literature. Finally, the concluding remarks in the deep IDS context are provided and possible directions at the subsequent studies are listed.

关键词： Intrusion detection Deep learning auto-encoder CNN DNN GAN LSTM

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On the identification of thyroid nodules using semi-supervised deep learning

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021年第3期37卷 e3433-e3433页

作者： Turk, Gamze Ozdemir, Mustafa Zeydan, Ruken Turk, Yekta Bilgin, Zeki Zeydan, Engin Training & Res Hosp Dept Radiol Kayseri Turkey Gazi Univ Fac Med Dept Chest Dis Ankara Turkey Arcelik Res & Dev Div Istanbul Turkey CTTC Commun Networks Div Barcelona Spain Ctr Tecnol Telecomunicac Catalunya Barcelona 08860 Spain

Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or expert-based methods. For this reason, the solutions backed by Machine Learning (ML) algorithms are key to improve the detection rates of such rare cases. In this paper, we investigate the application of ML in the healthcare domain for the detection of rare thyroid nodules. The utilized dataset is collected from 636 distinct patients in 99 unique days in Turkey. In addition to the texture feature data of the Ultrasound (US), we have also included the scores of different assessment methods created by different health institutions (e.g., Korean, American and European thyroid societies) as additional features. For detection of extremely rare malign cases, we use auto-encoder based neural network model. Through numerical results, it is shown that the auto-encoder based model can result in an average Recall score of 0.98 and a Sensitivity score of 1.00 for detecting malign and non-malign cases from the healthcare dataset outperforming the traditional classification algorithms that are trained after Synthetic Minority Oversampling Technique (SMOTE) oversampling.

关键词： auto-encoder classification malign semi-supervised deep learning thyroid nodule

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Defending Adversarial Examples via DNN Bottleneck Reinforcement 20

Defending Adversarial Examples via DNN Bottleneck Reinforcem...

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28th ACM International Conference on Multimedia (MM)

作者： Liu, Wenqing Shi, Miaojing Furon, Teddy Li, Li Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Kings Coll London London England Univ Rennes INRIA CNRS IRISA Rennes France Tongji Univ Coll Elect & Informat Engn Inst Intelligent Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781450379885

This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks. Typical DNN classifiers encode the input image into a compressed latent representation more suitable for inference. This information bottleneck makes a trade-off between the image-specific structure and class-specific information in an image. By reinforcing the former while maintaining the latter, any redundant information, be it adversarial or not, should be removed from the latent representation. Hence, this paper proposes to jointly train an auto-encoder (AE) sharing the same encoding weights with the visual classifier. In order to reinforce the information bottleneck, we introduce the multi-scale low-pass objective and multi-scale high-frequency communication for better frequency steering in the network. Unlike existing approaches, our scheme is the first reforming defense per se which keeps the classifier structure untouched without appending any pre-processing head and is trained with clean images only. Extensive experiments on MNIST, CIFAR-10 and ImageNet demonstrate the strong defense of our method against various adversarial attacks.

关键词： DNN adversarial attacks information bottleneck auto-encoder

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