13 citations · 14 across the 3 of their papers we have counts for
10 papers
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…
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…
Co-Attention for Conditioned Image Matching
Olivia Wiles, Sebastien Ehrhardt, Andrew Zisserman
We propose a new approach to determine correspondences between image pairs in the wild under large changes in illumination, viewpoint, context, and material. While other approaches…
SynSin: End-to-end View Synthesis from a Single Image
Olivia Wiles, Georgia Gkioxari, Richard Szeliski +1
Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D s…
Self-supervised learning of class embeddings from video
Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
This work explores how to use self-supervised learning on videos to learn a class-specific image embedding that encodes pose and shape information. At train time, two frames of the…