| iCaRL: Incremental Classifier and Representation Learning SA Rebuffi, A Kolesnikov, G Sperl, CH Lampert CVPR 2017, 2017 | 1433 | 2017 |
| Learning multiple visual domains with residual adapters SA Rebuffi, H Bilen, A Vedaldi NeurIPS 2017, 2017 | 401 | 2017 |
| Efficient parametrization of multi-domain deep neural networks SA Rebuffi, H Bilen, A Vedaldi CVPR 2018, 2018 | 223 | 2018 |
| 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 | 107 | 2016 |
| There and Back Again: Revisiting Backpropagation Saliency Methods SA Rebuffi, R Fong, X Ji, A Vedaldi CVPR 2020, 2020 | 67 | 2020 |
| Automatically discovering and learning new visual categories with ranking statistics K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman ICLR 2020, 2020 | 47 | 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 | 45 | 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 | 36 | 2020 |
| Improving Robustness using Generated Data S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, T Mann NeurIPS 2021, 2021 | 15 | 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 | 14 | 2021 |
| Data Augmentation Can Improve Robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann NeurIPS 2021, 2021 | 12 | 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 | 11 | 2021 |
| 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 | 10 | 2021 |
| A fine-grained analysis on distribution shift O Wiles, S Gowal, F Stimberg, SA Rebuffi, I Ktena, T Cemgil arXiv preprint arXiv:2110.11328, 2021 | 5 | 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 | | 2021 |
| 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 | | 2021 |
| Influence of the input data on learning deep representations SA Rebuffi University of Oxford, 2020 | | 2020 |
| LSD-C: Linearly Separable Deep Clusters–Supplementary Material– SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman | | |
Andrea VedaldiUniversity of OxfordVerified email at robots.ox.ac.uk
