| iCaRL: Incremental Classifier and Representation Learning SA Rebuffi, A Kolesnikov, G Sperl, CH Lampert CVPR 2017, 2017 | 4093 | 2017 |
| Learning multiple visual domains with residual adapters SA Rebuffi, H Bilen, A Vedaldi NeurIPS 2017, 2017 | 951 | 2017 |
| Efficient parametrization of multi-domain deep neural networks SA Rebuffi, H Bilen, A Vedaldi CVPR 2018, 2018 | 424 | 2018 |
| Data Augmentation Can Improve Robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann NeurIPS 2021, 2021 | 295 | 2021 |
| 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 | 267 | 2021 |
| Improving Robustness using Generated Data S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, T Mann NeurIPS 2021, 2021 | 263 | 2021 |
| A fine-grained analysis on distribution shift O Wiles, S Gowal, F Stimberg, SA Rebuffi, I Ktena, T Cemgil ICLR 2022, 2021 | 219 | 2021 |
| Automatically discovering and learning new visual categories with ranking statistics K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman ICLR 2020, 2020 | 212 | 2020 |
| Modeling of Store Gletscher's calving dynamics, West Greenland, in response to ocean thermal forcing M Morlighem, J Bondzio, H Seroussi, E Rignot, E Larour, A Humbert, ... Geophysical Research Letters 43 (6), 2659-2666, 2016 | 149 | 2016 |
| There and Back Again: Revisiting Backpropagation Saliency Methods SA Rebuffi, R Fong, X Ji, A Vedaldi CVPR 2020, 2020 | 137 | 2020 |
| Autonovel: Automatically discovering and learning novel visual categories K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 114 | 2021 |
| Semi-supervised learning with scarce annotations SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 | 57 | 2020 |
| Defending against image corruptions through adversarial augmentations DA Calian, F Stimberg, O Wiles, SA Rebuffi, A Gyorgy, T Mann, S Gowal ICLR 2022, 2021 | 46 | 2021 |
| 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 | 29 | 2024 |
| Lsd-c: Linearly separable deep clusters SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 28 | 2021 |
| Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts F Croce, SA Rebuffi, E Shelhamer, S Gowal CVPR 2023, 2023 | 16 | 2023 |
| Revisiting adapters with adversarial training SA Rebuffi, F Croce, S Gowal ICLR 2023, 2022 | 14 | 2022 |
| NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research J Bornschein, A Galashov, R Hemsley, A Rannen-Triki, Y Chen, ... arXiv preprint arXiv:2211.11747, 2022 | 13 | 2022 |
| 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 |
| Generative models improve fairness of medical classifiers under distribution shifts O Wiles, I Albuquerque, SA Rebuffi, R Tanno, AG Roy, S Azizi, D Belgrave, ... | 1 | 2023 |
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