  Anomaly detection is a classical problem in computer vision, namely the
determination of the normal from the abnormal when datasets are highly biased
towards one class (normal) due to the insufficient sample size of the other
class (abnormal). While this can be addressed as a supervised learning problem,
a significantly more challenging problem is that of detecting the
unknown/unseen anomaly case that takes us instead into the space of a
one-class, semi-supervised learning paradigm. We introduce such a novel anomaly
detection model, by using a conditional generative adversarial network that
jointly learns the generation of high-dimensional image space and the inference
of latent space. Employing encoder-decoder-encoder sub-networks in the
generator network enables the model to map the input image to a lower dimension
vector, which is then used to reconstruct the generated output image. The use
of the additional encoder network maps this generated image to its latent
representation. Minimizing the distance between these images and the latent
vectors during training aids in learning the data distribution for the normal
samples. As a result, a larger distance metric from this learned data
distribution at inference time is indicative of an outlier from that
distribution - an anomaly. Experimentation over several benchmark datasets,
from varying domains, shows the model efficacy and superiority over previous
state-of-the-art approaches.
