IAM

TAG»MACHINE LEARNING«

SEPTEMBER2023

PROJECT

Achieving accuracy, fair and private image classification.

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SEPTEMBER2023

PROJECT

Keeping track of generated images using watermarking.

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ARTICLE

Proper Robustness Evaluation of Confidence-Calibrated Adversarial Training in PyTorch

Properly evaluating defenses against adversarial examples has been difficult as adversarial attacks need to be adapted to each individual defense. This also holds for confidence-calibrated adversarial training, where robustness is obtained by rejecting adversarial examples based on their confidence. Thus, regular robustness metrics and attacks are not easily applicable. In this article, I want to discuss how to evaluate confidence-calibrated adversarial training in terms of metrics and attacks.

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JULY2023

PROJECT

Evaluating AI models with uncertain ground truth.

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ARTICLE

Generalizing Adversarial Robustness with Confidence-Calibrated Adversarial Training in PyTorch

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.

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ARTICLE

Some Research Ideas for Conformal Training

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.

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ARTICLE

Updated Results for Confidence-Calibrated Adversarial Training

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.

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ARTICLE

PhD Thesis on Robustness and Uncertainty in Deep Learning

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.

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NOVEMBER2022

PROJECT

An example of a custom TensorFlow operation implemented in C++.

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