  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 adversarial networks for training deep
models in unsupervised and semi-supervised settings, we propose an end-to-end
architecture for one-class classification. Our architecture is composed of two
deep networks, each of which trained by competing with each other while
collaborating to understand the underlying concept in the target class, and
then classify the testing samples. One network works as the novelty detector,
while the other supports it by enhancing the inlier samples and distorting the
outliers. The intuition is that the separability of the enhanced inliers and
distorted outliers is much better than deciding on the original samples. The
proposed framework applies to different related applications of anomaly and
outlier detection in images and videos. The results on MNIST and Caltech-256
image datasets, along with the challenging UCSD Ped2 dataset for video anomaly
detection illustrate that our proposed method learns the target class
effectively and is superior to the baseline and state-of-the-art methods.
