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). MACHAD is available via pytorch-ood. You can find example code here. How does it work? § The general idea is that we want a neural network $f_{\theta}: \mathcal{X} \rightarrow \mathcal{Z}$ that maps inputs from the input space to some lower …