  The authors have introduced a novel method for unsupervised anomaly detection
that utilises a newly introduced Memory Module in their paper. We validate the
authors claim that this helps improve performance by helping the network learn
prototypical patterns, and uses the learnt memory to reduce the representation
capacity of Convolutional Neural Networks. Further, we validate the efficacy of
two losses introduced by the authors, Separateness Loss and Compactness Loss
presented to increase the discriminative power of the memory items and the
deeply learned features. We test the efficacy with the help of t-SNE plots of
the memory items.
