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.
Automatic visual inspection of wire ropes is an important but challenging task, as anomalies in the rope are usually unobtrusive. Certainly, a reliable anomaly detection is essential to assure the safety of the ropes. A one-class classification approach for the automatic detection of anomalies in wire ropes is presented. Furthermore, the performance of different well-established features from the field of textural defect detection are compared with respect to this task. The faultless rope structure is thereby modeled by a Gaussian mixture model and outliers are regarded as anomaly. To prove the practical applicability, a careful evaluation of the presented approach is performed on real-life rope data. In doing so, a special interest was put on the robustness of the model with respect to unintentional outliers in the training and on its generalization ability given further data from an identically constructed rope. The results prove good recognition rates accompanied by a high generalization ability and robustness to outliers in the training set.  Detecting instances of unknown categories is an important task for a multitude of problems such as object recognition, event detection, and defect localization. This article investigates the use of Gaussian process (GP) priors for this area of research. Focusing on the task of one-class classification, we analyze different measures derived from GP regression and approximate GP classification. We also study important theoretical connections to other approaches and discuss their underlying assumptions. Experiments are performed using a large number of datasets and different image kernel functions. Our findings show that our approaches can outperform the well-known support vector data description approach indicating the high potential of Gaussian processes for one-class classification. Furthermore, we show the suitability of our methods in the area of attribute prediction, defect localization, bacteria recognition, and background subtraction. These applications and experiments highlight the easy applicability of our method as well as its state-of-the-art performance compared to established methods.