| iCaRL: Incremental Classifier and Representation Learning SA Rebuffi, A Kolesnikov, G Sperl, CH Lampert CVPR 2017, 2017 | 4614 | 2017 |
| Learning multiple visual domains with residual adapters SA Rebuffi, H Bilen, A Vedaldi NeurIPS 2017, 2017 | 1039 | 2017 |
| Data Augmentation Can Improve Robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann NeurIPS 2021, 2021 | 647* | 2021 |
| Efficient parametrization of multi-domain deep neural networks SA Rebuffi, H Bilen, A Vedaldi CVPR 2018, 2018 | 457 | 2018 |
| Improving Robustness using Generated Data S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, T Mann NeurIPS 2021, 2021 | 334 | 2021 |
| A fine-grained analysis on distribution shift O Wiles, S Gowal, F Stimberg, SA Rebuffi, I Ktena, T Cemgil ICLR 2022, 2021 | 247 | 2021 |
| Automatically discovering and learning new visual categories with ranking statistics K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman ICLR 2020, 2020 | 229 | 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 | 161 | 2016 |
| 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 | 151 | 2021 |
| There and Back Again: Revisiting Backpropagation Saliency Methods SA Rebuffi, R Fong, X Ji, A Vedaldi CVPR 2020, 2020 | 148 | 2020 |
| 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 30 (4), 1166-1173, 2024 | 67 | 2024 |
| 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 | 60 | 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 | 53 | 2021 |
| 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 |
| 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, ... Journal of Machine Learning Research 24 (308), 1-77, 2023 | 20 | 2023 |
| Imagen 3 J Baldridge, J Bauer, M Bhutani, N Brichtova, A Bunner, K Chan, Y Chen, ... arXiv preprint arXiv:2408.07009, 2024 | 19 | 2024 |
| Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts F Croce, SA Rebuffi, E Shelhamer, S Gowal CVPR 2023, 2023 | 18 | 2023 |
| Revisiting adapters with adversarial training SA Rebuffi, F Croce, S Gowal ICLR 2023, 2022 | 17 | 2022 |
| A fine-grained analysis on distribution shift. arXiv 2021 O Wiles, S Gowal, F Stimberg, S Alvise-Rebuffi, I Ktena, K Dvijotham, ... arXiv preprint arXiv:2110.11328, 0 | 5 | |
| 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 |
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