ICPR

Our Paper Multi-Class Hypersphere Anomaly Detection (MCHAD) has been accepted for presentation at the ICPR 2022. In summary, we propose a new loss function for learning neural networks that are able to detect anomalies in their inputs. Poster for MCHAD (PDF). How does it work? Omitting some details, the loss we propose has three different components, each of which we will explain in the following. Intra-Class Variance We want the $f(x)$ of one class to cluster as tightly around a class center …