Publications associées à Adaptive data augmentation for image classification

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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

Adaptive data augmentation for image classification

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Estimating and Improving the Robustness of Attributions in Text

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

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

Deep Learning for 3D Surface Modelling and Reconstruction

Adversarial Training Should Be Cast As a Non-Zero-Sum Game

Rethinking data augmentation for adversarial robustness

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

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

Improved calorimetric particle identification in NA62 using machine learning techniques

Fast Adversarial Training With Adaptive Step Size

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

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Estimating and Improving the Robustness of Attributions in Text

Deep Learning for 3D Surface Modelling and Reconstruction

Adversarial Training Should Be Cast As a Non-Zero-Sum Game

Rethinking data augmentation for adversarial robustness

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

Improved calorimetric particle identification in NA62 using machine learning techniques

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

Fast Adversarial Training With Adaptive Step Size