I am looking for full-time (applied) research opportunities in industry, involving (trustworthy and robust) machine learning or (3D) computer vision, starting early 2022. Check out my CV and get in touch on LinkedIn!


Qualcomm Innovation Fellowship Talk “Confidence-Calibrated Adversarial Training and Random Bit Error Training”

As part of the Qualcomm Innovation Fellowship 2019, I have a talk on the research produced throughout the academic year 2019/2020. This talk covers two exciting works on robustness: robustness against various types of adversarial examples using confidence-calibrated adversarial training (CCAT) and robustness against bit errors in the model’s quantized weights. The latter can be shown to be important to reduce the energy-consumption of accelerators for neural networks. In this article, I want to share the slides corresponding to the talk.


The slides can be downloaded here and the corresponding papers are available on ArXiv:

David Stutz, Matthias Hein, Bernt Schiele. Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. ICML, 2020.
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele. Bit Error Robustness for Energy-Efficient DNN Accelerators. MLSys, 2021.

What is your opinion on this article? Did you find it interesting or useful? Let me know your thoughts in the comments below: