IAM

TAG»COMPUTER VISION«

AUGUST2022

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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AUGUST2022

PROJECT

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

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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

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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JULY2021

PROJECT

Random and adversarial bit error robustness of DNNs for energy-efficient and secure DNN accelerators.

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JULY2021

PROJECT

Robust generalization and overfitting linked to flatness of robust loss surface in weight space.

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ARTICLE

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.

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ARTICLE

Recorded CVPR’21 CV-AML Workshop Outstanding Paper Talk “Bit Error Robustness for Energy-Efficient DNN Accelerators”

In June this year, my work on bit error robustness of deep neural networks (DNNs) was recognized as outstanding paper at the CVPR’21 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges (AML-CV). Thus, as part of the workshop, I prepared a 15 minute talk highlighting how robustness against bit errors in DNN weights can improve the energy-efficiency of DNN accelerators. In this article, I want to share the recording.

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ARTICLE

ArXiv Pre-Print “Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators”

Deep neural network (DNN) accelerators are popular due to reduced cost and energy compared to GPUs. To further reduce energy consumption, the operating voltage of the on-chip memory can be reduced. However, this injects random bit errors, directly impacting the (quantized) DNN weights. As result, improving DNN robustness against these bit errors can significantly improve energy efficiency. Similarly, these chips are subject to bit-level hardware- or software-based attacks. In this case, robustness against adversarial bit errors is required to improve security of DNN accelerators. Our paper presented in this article addresses both problems.

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