IAM

DAVIDSTUTZ

TAG»DEEP LEARNING«

ARTICLE

ICML 2022 Art of Robustness Paper “On Fragile Features and Batch Normalization in Adversarial Training”

While batch normalization has long been argued to increase adversarial vulnerability, it is still used in state-of-the-art adversarial training models. This is likely because of easier training and increased expressiveness. At the same time, recent papers argue that adversarial examples are partly caused by fragile features caused by learning spurious correlations. In this paper, we study the impact of batch normalization on utilizing these fragile features for robustness by fine-tuning only the batch normalization layers.

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05thAUGUST2022

PROJECT

RESEARCH Fragile Features, Batch Normalization and Adversarial Training Outline Abstract Paper Poster News & Updates This is work led by Nils Walter. Quick links: Paper | Poster Abstract Modern deep learning architecture utilize batch normalization (BN) to stabilize training and improve accuracy. It has been shown that the BN layers alone are surprisingly expressive. In […]

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04thAUGUST2022

PROJECT

Improving corruption and adversarial robustness by enhancing weak sub-networks.

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ARTICLE

Machine Learning Security Seminar Talk “Relating Adversarially Robust Generalization to Flat Minima”

This week I was honored to speak at the Machine Learning Security Seminar organized by the Pattern Recognition and Applications Lab at University of Cagliari. I presented my work on relating adversarial robustness to flatness in the robust loss landscape, also touching on the relationship to weight robustness. In this article, I want to share the recording and slides of this talk.

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ARTICLE

International Seminar on Distribution-Free Statistics Talk “Conformal Training: Learning Optimal Conformal Classifiers”

Last week, I had the pleasure to give a talk at the recently started Seminar on Distribution-Free Statistics organized by Anastasios Angelopoulos. Specifically, I talked about conformal training, a procedure allowing to train a classifier and conformal predictor end-to-end. This allows to optimize arbitrary losses defined directly on the confidence sets obtained through conformal prediction and can be shown to improve inefficiency and other metrics for any conformal predictor used at test time. In this article, I want to share the corresponding recording.

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ARTICLE

Code Released: Adversarial Robust Generalization and Flatness

The code for my ICCV’21 paper relating adversarial robustness to flatness in the (robust) loss landscape is now available on GitHub. The repository includes implementations of various adversarial attacks, adversarial training variants and “attacks” on model weights in order to measure robust flatness.

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ARTICLE

Math Machine Learning Seminar of MPI MiS and UCLA Talk “Relating Adversarial Robustness and Weight Robustness Through Flatness”

In October, I had the pleasure to present my recent work on adversarial robustness and flat minima at the math machine learning seminar of MPI MiS and UCLA organized by Guido Montúfar. The talk covers several aspects of my PhD research on adversarial robustness and robustness in terms of the model weights. This article shares abstract and recording of the talk.

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ARTICLE

ArXiv Pre-Print “Learning Optimal Conformal Classifiers”

Conformal prediction (CP) allows to take any classifier and turn it into a set predictor with a guarantee that the true class is included with use-specified probability. This allows to develop classifiers with sufficient guarantees for safe deployment in many domains. However, CP is usually used as a post-training calibration step. Our paper presented in this article presents a training procedure name conformal training allowing to train classifier and conformal predictor end-to-end. This can reduce the average confidence set size and allows to optimize arbitrary objectives defined directly on the predicted sets.

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21thOCTOBER2021

PROJECT

End-to-end training of deep neural networks and conformal predictors to reduce confidence set size and optimizer application-specific objectives.

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ARTICLE

Recorded ICCV’21 Talk “Relating Adversarially Robust Generalization to Flat Minima”

In October this year, my work on relating adversarially robust generalization to flat minima in the (robust) loss surface with respect to weight perturbations was presented at ICCV’21. As oral presentation at ICCV’21, I recorded a 12 minute talk highlighting the main insights how (robust) flatness can avoid robust overfitting of adversarial training and improve robustness against adversarial examples. In this article, I want to share the recording.

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