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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Robust image classification

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Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Residual-based attention in physics-informed neural networks

Estimating and Improving the Robustness of Attributions in Text

Deep Learning Generalization with Limited and Noisy Labels

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Transformer Models for Vision

Robust Training and Verification of Deep Neural Networks

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Deep Learning Generalization with Limited and Noisy Labels

Robust Training and Verification of Deep Neural Networks

Transformer Models for Vision

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Residual-based attention in physics-informed neural networks

Estimating and Improving the Robustness of Attributions in Text

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks