Adversarially Learned One-Class Classifier for Novelty Detection

@article{Sabokrou2018AdversariallyLO,
  title={Adversarially Learned One-Class Classifier for Novelty Detection},
  author={Mohammad Sabokrou and Mohammad Khalooei and Mahmood Fathy and Ehsan Adeli},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={3379-3388}
}
Novelty detection is the process of identifying the observation(s) that differ in some respect from the training observations (the target class). In reality, the novelty class is often absent during training, poorly sampled or not well defined. Therefore, one-class classifiers can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end deep network is a cumbersome task. In this paper, inspired by the success of generative… CONTINUE READING

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