Publications

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 …

Our paper, PyTorch-OOD: A library for Out-of-Distribution Detection based on PyTorch, has been presented at the CVPR 2022 Workshops. You can find the most recent version of the Python source code on GitHub. Abstract § Machine Learning models based on Deep Neural Networks behave unpredictably when presented with inputs that do not stem from the training distribution and sometimes make egregiously wrong predictions with high confidence. This property undermines the trustworthiness of systems …

Our Paper Addressing Randomness in Evaluation Protocols for Out-of-Distribution Detection has been accepted at the ICJAI 2021 Workshop for Artificial Intelligence for Anomalies and Novelties. In summary, we investigated the following phenomenon: when you train neural networks several times, and then measure their performance on some task, there is a certain variance in the performance measurements, since the results of experiments may vary based on several factors (that are effectively …

Inspired by David Kriesel’s talk “Spiegel-Mining”, a friend of mine and a professor from the Hochschule Magdeburg scraped a German website that regularly publishes reviews of social work literature and mined the resulting 18.000 articles, hoping for interesting insights. In an attempt to visualize the discourse, we created several topic maps, like the one below, which you can find on the accompanying (German) website. The colors represent the gender of the authors of the …