51 citations · 88 across the 8 of their papers we have counts for
20 papers
Hindering Adversarial Attacks with Implicit Neural Representations
Andrei A. Rusu, Dan A. Calian, Sven Gowal +1
We introduce the Lossy Implicit Network Activation Coding (LINAC) defence, an input transformation which successfully hinders several common adversarial attacks on CIFAR- class…
Revisiting adapters with adversarial training
Sylvestre-Alvise Rebuffi, Francesco Croce, Sven Gowal
While adversarial training is generally used as a defense mechanism, recent works show that it can also act as a regularizer. By co-training a neural network on clean and adversari…
Data Augmentation Can Improve Robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian +3
Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on reducing robust ove…
An Empirical Investigation of Learning from Biased Toxicity Labels
Neel Nanda, Jonathan Uesato, Sven Gowal
Collecting annotations from human raters often results in a trade-off between the quantity of labels one wishes to gather and the quality of these labels. As such, it is often only…
A Closer Look at the Adversarial Robustness of Information Bottleneck Models
Iryna Korshunova, David Stutz, Alexander A. Alemi +2
We study the adversarial robustness of information bottleneck models for classification. Previous works showed that the robustness of models trained with information bottlenecks ca…
Fixing Data Augmentation to Improve Adversarial Robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian +3
Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on both heuristics-dri…