Evaluating AI models with uncertain ground truth.
Taking adversarial training from this previous article as baseline, this article introduces a new, confidence-calibrated variant of adversarial training that addresses two significant flaws: First, trained with L∞ adversarial examples, adversarial training is not robust against L2 ones. Second, it incurs a significant increase in (clean) test error. Confidence-calibrated adversarial training addresses these problems by encouraging lower confidence on adversarial examples and subsequently rejecting them.
With our paper on conformal training, we showed how conformal prediction can be integrated into end-to-end training pipelines. There are so many interesting directions of how to improve and build upon conformal training. Unfortunately, I just do not have the bandwidth to pursue all of them. So, in this article, I want to share some research ideas so others can pick them up.
Since I worked on confidence-calibrated training (CCAT) some years ago, CCAT has been evaluated using novel attacks. In this article, I want to share some updated results and numbers and contrast the reported numbers with newer experiments that I ran.
In March this year I finally submitted my PhD thesis and successfully defended in July. Now, more than 6 months later, my thesis is finally available in the university’s library. During my PhD, I worked on various topics surrounding robustness and uncertainty in deep learning, including adversarial robustness, robustness to bit errors, out-of-distribution detection and conformal prediction. In this article, I want to share my thesis and give an overview of its contents.
An example of a custom TensorFlow operation implemented in C++.
Tutorials for (deep convolutional) neural networks.
PhD thesis on uncertainty estimation and (adversarial) robustness in deep learning.
In July this year I finally defended my PhD which mainly focused on (adversarial) robustness and uncertainty estimation in deep learning. In my case, the defense consisted of a (public) 30 minute talk about my work, followed by questions from the thesis committee and audience. In this article, I want to share the slides and some lessons learned in preparing for my defense.