Abstract
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