no code implementations • 25 Apr 2024 • Olivia Wiles, Chuhan Zhang, Isabela Albuquerque, Ivana Kajić, Su Wang, Emanuele Bugliarello, Yasumasa Onoe, Chris Knutsen, Cyrus Rashtchian, Jordi Pont-Tuset, Aida Nematzadeh
Human-rated prompt sets are generally small and the reliability of the ratings -- and thereby the prompt set used to compare models -- is not evaluated.
no code implementations • 18 Apr 2023 • Ira Ktena, Olivia Wiles, Isabela Albuquerque, Sylvestre-Alvise Rebuffi, Ryutaro Tanno, Abhijit Guha Roy, Shekoofeh Azizi, Danielle Belgrave, Pushmeet Kohli, Alan Karthikesalingam, Taylan Cemgil, Sven Gowal
In our work, we show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models.
no code implementations • 27 Feb 2023 • Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, Borja Balle
By privately fine-tuning ImageNet pre-trained diffusion models with more than 80M parameters, we obtain SOTA results on CIFAR-10 and Camelyon17 in terms of both FID and the accuracy of downstream classifiers trained on synthetic data.
no code implementations • 6 Oct 2022 • Olivia Wiles, Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski
In this work, we propose a framework enabling research on hour-long videos with the same hardware that can now process second-long videos.
no code implementations • 18 Aug 2022 • Olivia Wiles, Isabela Albuquerque, Sven Gowal
Misclassified inputs are clustered and a captioning model is used to describe each cluster.
1 code implementation • NeurIPS 2021 • Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian, Florian Stimberg, Olivia Wiles, Timothy Mann
Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training.
no code implementations • ICLR 2022 • Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre Alvise-Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, Taylan Cemgil
Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts.
1 code implementation • NeurIPS 2021 • Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, Timothy Mann
Against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our models achieve 66. 10% and 33. 49% robust accuracy on CIFAR-10 and CIFAR-100, respectively (improving upon the state-of-the-art by +8. 96% and +3. 29%).
no code implementations • ICML Workshop AML 2021 • Iryna Korshunova, David Stutz, Alexander A. Alemi, Olivia Wiles, Sven Gowal
We study the adversarial robustness of information bottleneck models for classification.
no code implementations • ICLR 2022 • Dan A. Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, Sven Gowal
Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog.
6 code implementations • 2 Mar 2021 • Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian, Florian Stimberg, Olivia Wiles, Timothy Mann
In particular, against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our model reaches 64. 20% robust accuracy without using any external data, beating most prior works that use external data.
no code implementations • CVPR 2021 • 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.
3 code implementations • CVPR 2020 • Olivia Wiles, Georgia Gkioxari, Richard Szeliski, Justin Johnson
Single image view synthesis allows for the generation of new views of a scene given a single input image.
no code implementations • 28 Oct 2019 • 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.
no code implementations • 6 Sep 2018 • Olivia Wiles, Andrew Zisserman
Finally, we demonstrate that we can indeed obtain a depth map of a novel object from a single image for a variety of sculptures with varying shape/texture, and that the network generalises at test time to new domains (e. g. synthetic images).
no code implementations • ECCV 2018 • 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).
2 code implementations • 21 Aug 2018 • 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.
Ranked #2 on Unsupervised Facial Landmark Detection on 300W
no code implementations • 27 Jul 2018 • 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).
no code implementations • 21 Nov 2017 • Olivia Wiles, Andrew Zisserman
The objective of this paper is 3D shape understanding from single and multiple images.