111 citations · 294 across the 14 of their papers we have counts for
17 papers
Generative models improve fairness of medical classifiers under distribution shifts
Ira Ktena, Olivia Wiles, Isabela Albuquerque +9
A ubiquitous challenge in machine learning is the problem of domain generalisation. This can exacerbate bias against groups or labels that are underrepresented in the datasets used…
Differentially Private Diffusion Models Generate Useful Synthetic Images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal +7
The ability to generate privacy-preserving synthetic versions of sensitive image datasets could unlock numerous ML applications currently constrained by data availability. Due to t…
Compressed Vision for Efficient Video Understanding
Olivia Wiles, Joao Carreira, Iain Barr +2
Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The vast majority of computer vision research, however, still focuses on indiv…
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning
Olivia Wiles, Isabela Albuquerque, Sven Gowal
Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and te…
A Fine-Grained Analysis on Distribution Shift
Olivia Wiles, Sven Gowal, Florian Stimberg +4
Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying…
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…
Improving Robustness using Generated Data
Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles +3
Recent work argues that robust training requires substantially larger datasets than those required for standard classification. On CIFAR-10 and CIFAR-100, this translates into a si…
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…
Defending Against Image Corruptions Through Adversarial Augmentations
Dan A. Calian, Florian Stimberg, Olivia Wiles +4
Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this p…
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…
3D Surface Reconstruction by Pointillism
Olivia Wiles, Andrew Zisserman
The objective of this work is to infer the 3D shape of an object from a single image. We use sculptures as our training and test bed, as these have great variety in shape and appea…
Self-supervised learning of a facial attribute embedding from video
Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we i…
X2Face: A network for controlling face generation by using images, audio, and pose codes
Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be use…
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
Olivia Wiles, Andrew Zisserman
The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that ca…