  In this paper, we propose a novel method for irregularity detection. Previous
researches solve this problem as a One-Class Classification (OCC) task where
they train a reference model on all of the available samples. Then, they
consider a test sample as an anomaly if it has a diversion from the reference
model. Generative Adversarial Networks (GANs) have achieved the most promising
results for OCC while implementing and training such networks, especially for
the OCC task, is a cumbersome and computationally expensive procedure. To cope
with the mentioned challenges, we present a simple but effective method to
solve the irregularity detection as a binary classification task in order to
make the implementation easier along with improving the detection performance.
We learn two deep neural networks (generator and discriminator) in a GAN-style
setting on merely the normal samples. During training, the generator gradually
becomes an expert to generate samples which are similar to the normal ones. In
the training phase, when the generator fails to produce normal data (in the
early stages of learning and also prior to the complete convergence), it can be
considered as an irregularity generator. In this way, we simultaneously
generate the irregular samples. Afterward, we train a binary classifier on the
generated anomalous samples along with the normal instances in order to be
capable of detecting irregularities. The proposed framework applies to
different related applications of outlier and anomaly detection in images and
videos, respectively. The results confirm that our proposed method is superior
to the baseline and state-of-the-art solutions.
