One-class classification (OCC) aims to learn an effective data description enclose all normal training samples and detect anomalies based on the deviation from description. Current state-of-the-art OCC models a compact normality by hyper-sphere minimisation, but they often suffer overfitting data, especially when set is small or contaminated with anomalous samples. To address this issue, we int...

We propose a method that can perform one-class classification given only small number of examples from the target class and none others. formulate learning meaningful features for as meta-learning problem in which meta-training stage repeatedly simulates classification, using loss chosen algorithm to learn feature representation. To these representations, we require multiclass data similar task...

Although few-shot learning and one-class classification (OCC), i.e., a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the OCC problem presents method to modify episodic sampling strategy of model-agnostic meta-learning (MAML) algorithm learn model initialization particularly suited for tasks. T...

Project co-funded by the European Commission within the 6th Framework Programme Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission services) RE Restricted to a group specified by the consortium (including the Commission services) CO Confidential, only for members of the consortium (including the Commission services) The information in this do...

Dissemination Level PU Public x PP Restricted to other programme participants (including the commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

One-class classifiers employing for training only the data from one class are justified when the data from other classes is difficult to obtain. In particular, their use is justified in mobile-masquerader detection, where user characteristics are classified as belonging to the legitimate user class or to the impostor class, and where collecting the data originated from impostors is problematic....