| X2face: A network for controlling face generation using images, audio, and pose codes O Wiles, A Koepke, A Zisserman Proceedings of the European conference on computer vision (ECCV), 670-686, 2018 | 426 | 2018 |
| Synsin: End-to-end view synthesis from a single image O Wiles, G Gkioxari, R Szeliski, J Johnson Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 370 | 2020 |
| Fixing data augmentation to improve adversarial robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann arXiv preprint arXiv:2103.01946, 2021 | 234 | 2021 |
| Data augmentation can improve robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, TA Mann Advances in Neural Information Processing Systems 34, 29935-29948, 2021 | 214 | 2021 |
| Improving robustness using generated data S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, TA Mann Advances in Neural Information Processing Systems 34, 4218-4233, 2021 | 208 | 2021 |
| A fine-grained analysis on distribution shift O Wiles, S Gowal, F Stimberg, S Alvise-Rebuffi, I Ktena, K Dvijotham, ... arXiv preprint arXiv:2110.11328, 2021 | 179 | 2021 |
| Self-supervised learning of a facial attribute embedding from video O Wiles, A Koepke, A Zisserman arXiv preprint arXiv:1808.06882, 2018 | 131 | 2018 |
| SilNet: Single-and Multi-View Reconstruction by Learning from Silhouettes O Wiles, A Zisserman British Machine Vision Conference (BMVC), 2017 | 116 | 2017 |
| D2d: Learning to find good correspondences for image matching and manipulation O Wiles, S Ehrhardt, A Zisserman Proceedings of the IEEE/CVF Conference on Computer Vision, 2021 | 57* | 2021 |
| Defending against image corruptions through adversarial augmentations DA Calian, F Stimberg, O Wiles, SA Rebuffi, A Gyorgy, T Mann, S Gowal arXiv preprint arXiv:2104.01086, 2021 | 45 | 2021 |
| Sight to sound: An end-to-end approach for visual piano transcription AS Koepke, O Wiles, Y Moses, A Zisserman ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 34 | 2020 |
| Differentially private diffusion models generate useful synthetic images S Ghalebikesabi, L Berrada, S Gowal, I Ktena, R Stanforth, J Hayes, S De, ... arXiv preprint arXiv:2302.13861, 2023 | 30 | 2023 |
| Discovering bugs in vision models using off-the-shelf image generation and captioning O Wiles, I Albuquerque, S Gowal arXiv preprint arXiv:2208.08831, 2022 | 19 | 2022 |
| Learning to predict 3d surfaces of sculptures from single and multiple views O Wiles, A Zisserman International Journal of Computer Vision 127 (11), 1780-1800, 2019 | 13 | 2019 |
| Visual pitch estimation A Koepke, O Wiles, A Zisserman Sound and Music Computing Conference, 2019 | 11 | 2019 |
| Generative models improve fairness of medical classifiers under distribution shifts I Ktena, O Wiles, I Albuquerque, SA Rebuffi, R Tanno, AG Roy, S Azizi, ... Nature Medicine, 1-8, 2024 | 9 | 2024 |
| Compressed vision for efficient video understanding O Wiles, J Carreira, I Barr, A Zisserman, M Malinowski Proceedings of the Asian Conference on Computer Vision, 4581-4597, 2022 | 6 | 2022 |
| A closer look at the adversarial robustness of information bottleneck models I Korshunova, D Stutz, AA Alemi, O Wiles, S Gowal arXiv preprint arXiv:2107.05712, 2021 | 3 | 2021 |
| A fine-grained analysis of robustness to distribution shifts O Wiles, S Gowal, F Stimberg, SA Rebuffi, I Ktena, KD Dvijotham, ... NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021 | 2 | 2021 |
| Doing more with less: Improving robustness using generated data S Gowal, SA Rebuffi, O Wiles, F Stimberg, D Calian, T Mann, L DeepMind ICLR Workshop on Security and Safety in Machine Learning Systems, 2021 | 2 | 2021 |
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