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Sylvestre-Alvise Rebuffi
Sylvestre-Alvise Rebuffi
Google DeepMind
Verified email at deepmind.com
Title
Cited by
Cited by
Year
iCaRL: Incremental Classifier and Representation Learning
SA Rebuffi, A Kolesnikov, G Sperl, CH Lampert
CVPR 2017, 2017
33802017
Learning multiple visual domains with residual adapters
SA Rebuffi, H Bilen, A Vedaldi
NeurIPS 2017, 2017
8112017
Efficient parametrization of multi-domain deep neural networks
SA Rebuffi, H Bilen, A Vedaldi
CVPR 2018, 2018
3812018
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
2262021
Improving Robustness using Generated Data
S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, T Mann
NeurIPS 2021, 2021
2022021
Data Augmentation Can Improve Robustness
SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann
NeurIPS 2021, 2021
2002021
A fine-grained analysis on distribution shift
O Wiles, S Gowal, F Stimberg, SA Rebuffi, I Ktena, T Cemgil
ICLR 2022, 2021
1702021
Automatically discovering and learning new visual categories with ranking statistics
K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman
ICLR 2020, 2020
1702020
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
1362016
There and Back Again: Revisiting Backpropagation Saliency Methods
SA Rebuffi, R Fong, X Ji, A Vedaldi
CVPR 2020, 2020
1262020
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
882021
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
522020
Defending against image corruptions through adversarial augmentations
DA Calian, F Stimberg, O Wiles, SA Rebuffi, A Gyorgy, T Mann, S Gowal
ICLR 2022, 2021
442021
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
272021
Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
F Croce, SA Rebuffi, E Shelhamer, S Gowal
CVPR 2023, 2023
112023
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
102023
Generative models improve fairness of medical classifiers under distribution shifts
I Ktena, O Wiles, I Albuquerque, SA Rebuffi, R Tanno, AG Roy, S Azizi, ...
arXiv preprint arXiv:2304.09218, 2023
92023
Revisiting adapters with adversarial training
SA Rebuffi, F Croce, S Gowal
ICLR 2023, 2022
82022
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
22021
Adversarially self-supervised pre-training improves accuracy and robustness
SA Rebuffi, O Wiles, E Shelhamer, S Gowal
12023
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