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 …

Our companion paper, On Challenging Aspects of Reproducibility in Deep Anomaly Detection, has been accepted for presentation at the Fourth Workshop on Reproducible Research in Pattern Recognition (satellite event of ICPR 2022). In it, we discuss aspects of reproducibility for our anomaly detection algorithm MCHAD, as well as anomaly detection with deep neural networks in general. In particular, we discussed the following challenges for the reproducibility: Nondeterminism: conducting the same …