We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss are proposed along with a parallel CNN architecture. A template matching-based framework is introduced to facilitate the testing process. Extensive experiments on publicly available anomaly detection, novelty detection and mobile active authentication datasets show that the proposed Deep One-Class (DOC) classification method achieves significant improvements over the state-of-the-art.
In @cite_16 , visual anomalies in wire ropes are detected based on Gaussian process modeling. Anomaly detection is performed by maximizing KL divergence in @cite_35 , where the underlying distribution is assumed to be a known Gaussian. A detailed description of various anomaly detection methods can be found in @cite_3 .
@cite_35 @cite_16 @cite_3
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We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals. Anomaly Detection in Wire Ropes is an important problem. Detecting suspicious anomalies in the rope surface is challenging because of the variety of its visual appearance caused by reflections or mud on the rope surface. This hinders the discrimination between uncritical variations and small defects within the rope surface enormously. The fact that nearly no defective samples are available to train a supervised system relates this problem to the concept of one-class classification (OCC). In this work we show how to utilize one-class classification with Gaussian processes (GP) to detect anomalies in wire ropes. The method allows modeling the distribution of non-defective data in a non-parametric manner. Furthermore, it is really easy to implement (few lines of code), embedded in a Bayesian framework, and can be used with arbitrary kernel functions. Therefore, it is suitable for a wide range of defect localization applications. Our experiments, performed on two real ropes, demonstrate that the GP framework for OCC clearly outperforms former approaches for anomaly detection in wire ropes. The obtained results are comparable with or even outperform those obtained with the Support Vector Data Description, which is the state of the art reference in the field of one-class classification. 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.