Re Learning Memory Guided Normality for Anomaly Detection
This addresses anomaly detection in unsupervised settings, but appears incremental as it builds on existing memory-based methods with specific enhancements.
The authors tackled unsupervised anomaly detection by introducing a Memory Module to learn prototypical patterns and reduce CNN representation capacity, resulting in improved performance validated through t-SNE plots and two new losses (Separateness and Compactness Loss).
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.