Confidence-calibrated adversarial training (CCAT) addresses two problems when training on adversarial examples: the lack of robustness against adversarial examples unseen during training, and the reduced (clean) accuracy. In particular, CCAT biases the model towards predicting low-confidence on adversarial examples such that adversarial examples can be rejected by confidence thresholding. In this article, I want to share the slides of the corresponding ICML talk.
Recently, I had the opportunity to present my work on confidence-calibrated adversarial training at the Bosch Center for Artifical Intelligence and the University of Tübingen, specifically, the newly formed Tübingen AI Center. As part of the talk, I outlined the motivation and strengths of confidence-calibrated adversarial training compared to standard adversarial training: robustness against previously unseen attacks and improved accuracy. I also touched on the difficulties faced during robustness evaluation. This article provides the corresponding slides and gives a short overview of the talk.
In April, I visited Prof. Bernt Schiele’s Computer Vision and Multimodal Computing Department at the Max Planck Institute for Informatics in Saarbrücken. Aside from a presentation on my recent superpixel benchmark, I also met many interesting people and learned a lot about a career in research.
In the course of a seminar on “Selected Topics in Image Processing”, I worked on iPiano, an algorithm for non-convex and non-smooth optimization proposed by Ochs et al. . iPiano combines forward-backward splitting with an inertial force. This article presents the corresponding seminar paper including an implementation in C++ with applications to image denoising, image segmentation and compressed sensing.
In the course of my second seminar on “Current Topics in Computer Vision and Machine Learning”, offered by the Computer Vision Group at RWTH Aachen University, I wrote a report entitled “Neural Codes for Image Retrieval”. The work is motivated by recent research by Bebanko et al.  and the report as well as the corresponding slides can be found here.
At RWTH Aachen University, after writing a bachelor thesis, one has to give a final talk on the topic. My bachelor thesis “Superpixel Segmentation using Depth Information” examines the use of depth information to enhance superpixel segmentation by extending the superpixel algorithm called SEEDS . This article presents the slides of my final talk.
In my sixth semester at RWTH Aachen University, I am currently attending a seminar offered by the Computer Vision Group headed by Prof. Leibe on “Current Topics in Computer Vision and Machine Learning”. In the course of this seminar, I wrote a seminar paper entitled “Understanding Convolutional Neural Networks”. Both the seminar paper as well as the slides of the corresponding talk can be found here.
During my fifth semester at RWTH Aachen University I participated in a seminar offered by the Chair of Computer Science 6 on “Selected Topics in Human Language Technology and Pattern Recognition”. As result, I wrote a seminar paper on neural networks for pattern recognition titled “Introduction to Neural Networks”. At the end of the seminar, all participants had to give a short talk on their topic. Both my seminar paper and my slides of the talk can be found here.