Learning Deep Features for One-Class Classification

Code

• github.com/PramuPerera/DeepOneClass
  OfficialIn papercaffe2★ 0
• github.com/llllxt/oneclass-deep-feature
  none★ 0
• github.com/PINTO0309/Keras-OneClassAnomalyDetection
  tf★ 0
• github.com/buiduchanh/Learning-Deep-Features-for-One-Class-Classification
  none★ 0
• github.com/Goulustis/One_Class_MNIST
  pytorch★ 0

Abstract

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.

Tasks

Reproductions