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      \field{title}{XLI. On discordant observations}
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      \field{booktitle}{Medical Imaging 2018: Computer-Aided Diagnosis}
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      \field{title}{{Anomaly detection for medical images based on a one-class classification}}
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        {{hash=2329606ee30094bfea6d6c7b6f1ede90}{%
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      \field{journaltitle}{IEEE Communications Surveys \& Tutorials}
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      \field{title}{Anomaly Detection in Blockchain Networks: A Comprehensive Survey}
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      \field{year}{2023}
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      \keyw{Blockchains;Anomaly detection;Security;Smart contracts;Privacy;Bitcoin;Tutorials;Blockchain;anomaly detection;fraud detection}
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      \field{abstract}{Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.}
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      \field{journaltitle}{Sensors}
      \field{number}{5}
      \field{title}{Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound}
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      \field{year}{2018}
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    \entry{anomaly_detection_survey}{article}{}{}
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        {New York, NY, USA}%
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        {Association for Computing Machinery}%
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      \field{abstract}{Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.}
      \field{issn}{0360-0300}
      \field{journaltitle}{ACM Comput. Surv.}
      \field{month}{7}
      \field{number}{3}
      \field{title}{Anomaly detection: A survey}
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      \field{year}{2009}
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      \keyw{outlier detection,Anomaly detection}
    \endentry
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      \field{title}{Deep Semi-Supervised Anomaly Detection}
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      \field{year}{2019}
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      \field{booktitle}{2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}
      \field{month}{1}
      \field{title}{Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors}
      \field{year}{2023}
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      \field{journaltitle}{Remote Sensing}
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      \field{number}{10}
      \field{title}{Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm}
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      \field{title}{Some Studies in Machine Learning Using the Game of Checkers}
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      \field{year}{1959}
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      \field{abstract}{Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.}
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      \field{abstract}{Autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality reduction method. With the development of deep learning technology, autoencoder has attracted the attention of many scholars. Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles. The related autoencoder models are comprehensively analyzed and discussed. This paper introduces the application progress of autoencoders in different fields, such as image classification and natural language processing, etc. Finally, the shortcomings of the current autoencoder algorithm are summarized, and prospected for its future development directions are addressed.}
      \field{issn}{1568-4946}
      \field{journaltitle}{Applied Soft Computing}
      \field{title}{A comprehensive survey on design and application of autoencoder in deep learning}
      \field{volume}{138}
      \field{year}{2023}
      \field{pages}{110176}
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      \keyw{Deep learning,Autoencoder,Unsupervised learning,Feature extraction,Autoencoder application}
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