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

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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

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

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Rethinking data augmentation for adversarial robustness

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

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

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Robust direct acoustic impedance control using two microphones for mixed feedforward-feedback controller

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Improving the Training of Compact Neural Networks for Visual Recognition

Understanding Deep Neural Networks using Adversarial Attacks

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Rethinking data augmentation for adversarial robustness

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Robust direct acoustic impedance control using two microphones for mixed feedforward-feedback controller

Understanding Deep Neural Networks using Adversarial Attacks

Improving the Training of Compact Neural Networks for Visual Recognition

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