scholarly journals The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

2021 ◽  
Author(s):  
Paul Bergmann ◽  
Kilian Batzner ◽  
Michael Fauser ◽  
David Sattlegger ◽  
Carsten Steger
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Structural Changes ◽  
Performance Metrics ◽  
Generative Models ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Natural Image ◽  
Advantages And Disadvantages ◽  
Unsupervised Anomaly Detection

AbstractThe detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec anomaly detection dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth annotations for all anomalies. We conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pretrained convolutional neural networks, as well as classical computer vision methods. We highlight the advantages and disadvantages of multiple performance metrics as well as threshold estimation techniques. This benchmark indicates that methods that leverage descriptors of pretrained networks outperform all other approaches and deep-learning-based generative models show considerable room for improvement.

scholarly journals Recent Advancement in Anomaly Detection in Surveillance Videos

2019 ◽  
Vol 8 (2) ◽  
pp. 964-970
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Video Data ◽  
Detection Methods ◽  
Future Research ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Computer ◽  
Banking Education ◽  
To Come

Detection of Anomaly is of a notable and emergent problem into many diverse fields like information theory, deep learning, computer vision, machine learning, and statistics that have been researched within the various application from diverse domains including agriculture, health care, banking, education, and transport anomaly detection. Newly, numbers of important anomaly detection techniques along with diverseness of sort have been watched. The main aim of this paper to come up with a broad summary of the present development on detection of an anomaly, exclusively for video data with mixed types and high dimensionalities, where identifying the anomalous behaviors and event or anomalous patterns is a significant task. The paper expresses the advantages and disadvantages of the detection methods the experiments tried on the publically available benchmark dataset to assess numerous popular and classical methods and models. The objective of this analysis is to furnish an understanding of recent computer vision and machine algorithms methods and also state-of-the-art deep learnings techniques to detect anomalies for researchers. At last, the paper delivered roughly directions for future research on an anomalies detection.

scholarly journals Uncertainty-Aware Deep Classifiers Using Generative Models

2020 ◽  
Vol 34 (04) ◽  
pp. 5620-5627 ◽  
Author(s):  
Murat Sensoy ◽  
Lance Kaplan ◽  
Federico Cerutti ◽  
Maryam Saleki
Keyword(s):  
Neural Networks ◽  
Epistemic Uncertainty ◽  
Feature Space ◽  
Generative Models ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Bayesian Approaches ◽  
Data Set ◽  
Auxiliary Data

Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.

scholarly journals FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2451 ◽  
Author(s):  
Mohsin Munir ◽  
Shoaib Ahmed Siddiqui ◽  
Muhammad Ali Chattha ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Internal State ◽  
Arima Model ◽  
Streaming Data ◽  
Detection Technique ◽  
Detection Methods ◽  
Smart Devices ◽  
Detection Techniques ◽  
Unsupervised Anomaly Detection

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors record the internal state of a machine, the external environment, and the interaction of machines with other machines and humans. It is of prime importance to leverage this information in order to minimize downtime of machines, or even avoid downtime completely by constant monitoring. Since each device generates a different type of streaming data, it is normally the case that a specific kind of anomaly detection technique performs better than the others depending on the data type. For some types of data and use-cases, statistical anomaly detection techniques work better, whereas for others, deep learning-based techniques are preferred. In this paper, we present a novel anomaly detection technique, FuseAD, which takes advantage of both statistical and deep-learning-based approaches by fusing them together in a residual fashion. The obtained results show an increase in area under the curve (AUC) as compared to state-of-the-art anomaly detection methods when FuseAD is tested on a publicly available dataset (Yahoo Webscope benchmark). The obtained results advocate that this fusion-based technique can obtain the best of both worlds by combining their strengths and complementing their weaknesses. We also perform an ablation study to quantify the contribution of the individual components in FuseAD, i.e., the statistical ARIMA model as well as the deep-learning-based convolutional neural network (CNN) model.

scholarly journals egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5895
Author(s):  
Jiansu Pu ◽  
Jingwen Zhang ◽  
Hui Shao ◽  
Tingting Zhang ◽  
Yunbo Rao
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Communication Networks ◽  
Social Communication ◽  
Detection Method ◽  
Detection Methods ◽  
Visualization System ◽  
Egocentric Network ◽  
Unsupervised Anomaly Detection ◽  
The Relationship

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks.

scholarly journals Evaluation of Unsupervised Anomaly Detection Methods in Sentiment Mining

2019 ◽  
Vol 8 (9) ◽  
pp. 1080-1085
Keyword(s):  
Anomaly Detection ◽  
Computation Time ◽  
Vital Role ◽  
Detection Methods ◽  
Research Focus ◽  
Comparative Performance ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Sentiment Mining ◽  
Unsupervised Anomaly Detection

Anomaly detection has vital role in data preprocessing and also in the mining of outstanding points for marketing, network sensors, fraud detection, intrusion detection, stock market analysis. Recent studies have been found to concentrate more on outlier detection for real time datasets. Anomaly detection study is at present focuses on the expansion of innovative machine learning methods and on enhancing the computation time. Sentiment mining is the process to discover how people feel about a particular topic. Though many anomaly detection techniques have been proposed, it is also notable that the research focus lacks a comparative performance evaluation in sentiment mining datasets. In this study, three popular unsupervised anomaly detection algorithms such as density based, statistical based and cluster based anomaly detection methods are evaluated on movie review sentiment mining dataset. This paper will set a base for anomaly detection methods in sentiment mining research. The results show that density based (LOF) anomaly detection method suits best for the movie review sentiment dataset.

scholarly journals Recent Advances in Anomaly Detection Methods Applied to Aviation

Aerospace ◽  
2019 ◽  
Vol 6 (11) ◽  
pp. 117 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

A Comparative of 3D Surface Extraction Methods for Potential Metrology Applications

2014 ◽  
Vol 615 ◽  
pp. 15-21
Author(s):  
Sinué Ontiveros-Zepeda ◽  
José Antonio Yagüe-Fabra ◽  
Roberto Jiménez Pacheco ◽  
Francisco Javier Brosed-Dueso
Keyword(s):  
Computer Vision ◽  
Edge Detection ◽  
Extraction Methods ◽  
Point Of View ◽  
Detection Methods ◽  
Volume Data ◽  
Surface Extraction ◽  
Advantages And Disadvantages ◽  
Factors Influencing ◽  
Number Of Factors

The number of factors influencing the CT process for metrology applications increases its complexity and cause the loss of accuracy during CT measurements. One of the most critical is the edge detection also called surface extraction or image segmentation, which is the process of surface formation from the CT`s volume data. This paper presents different edge detection methods commonly used in areas like machine and computer vision and they are analyzed as an alternative to the commonly and commercially used for CT metrology applications. Each method is described and analyzed separately in order to highlight its advantages and disadvantages from a metrological point of view. An experimental comparative between two of them is also shown.

Unsupervised modeling anomaly detection in discussion forums posts using global vectors for text representation

2020 ◽  
Vol 26 (5) ◽  
pp. 551-578
Author(s):  
Paweł Cichosz
Keyword(s):  
Anomaly Detection ◽  
English Language ◽  
Learning Task ◽  
Detection Methods ◽  
Support Vector ◽  
Discussion Forums ◽  
Text Data ◽  
Polish Language ◽  
Unsupervised Anomaly Detection ◽  
Detection Quality

AbstractAnomaly detection can be seen as an unsupervised learning task in which a predictive model created on historical data is used to detect outlying instances in new data. This work addresses possibly promising but relatively uncommon application of anomaly detection to text data. Two English-language and one Polish-language Internet discussion forums devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana, serve as text sources that are both realistic and possibly interesting on their own, due to potential associations with drug-related crime. The utility of two different vector text representations is examined: the simple bag of words representation and a more refined Global Vectors (GloVe) representation, which is an example of the increasingly popular word embedding approach. They are both combined with two unsupervised anomaly detection methods, based on one-class support vector machines (SVM) and based on dissimilarity to k-medoids clusters. The GloVe representation is found definitely more useful for anomaly detection, permitting better detection quality and ameliorating the curse of dimensionality issues with text clustering. The cluster dissimilarity approach combined with this representation outperforms one-class SVM with respect to detection quality and appears a more promising approach to anomaly detection in text data.

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