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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

Laboratoire de systèmes d'information et d'inférence

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Regularization Techniques for Low-Resource Machine Translation

BLACK-BOX ATTACKS ON IMAGE ACTIVITY PREDICTION AND ITS NATURAL LANGUAGE EXPLANATIONS

Leveraging Unlabeled Data to Track Memorization

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

Penalising the biases in norm regularisation enforces sparsity

Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

A new variable shape parameter strategy for RBF approximation using neural networks

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

Fast Adversarial Training With Adaptive Step Size

A new variable shape parameter strategy for RBF approximation using neural networks

Leveraging Unlabeled Data to Track Memorization

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

BLACK-BOX ATTACKS ON IMAGE ACTIVITY PREDICTION AND ITS NATURAL LANGUAGE EXPLANATIONS

Regularization Techniques for Low-Resource Machine Translation

Penalising the biases in norm regularisation enforces sparsity

Fast Adversarial Training With Adaptive Step Size

Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

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