  We present a novel method for image anomaly detection, where algorithms that
use samples drawn from some distribution of "normal" data, aim to detect
out-of-distribution (abnormal) samples. Our approach includes a combination of
encoder and generator for mapping an image distribution to a predefined latent
distribution and vice versa. It leverages Generative Adversarial Networks to
learn these data distributions and uses perceptual loss for the detection of
image abnormality. To accomplish this goal, we introduce a new similarity
metric, which expresses the perceived similarity between images and is robust
to changes in image contrast. Secondly, we introduce a novel approach for the
selection of weights of a multi-objective loss function (image reconstruction
and distribution mapping) in the absence of a validation dataset for
hyperparameter tuning. After training, our model measures the abnormality of
the input image as the perceptual dissimilarity between it and the closest
generated image of the modeled data distribution. The proposed approach is
extensively evaluated on several publicly available image benchmarks and
achieves state-of-the-art performance.
