Sylvestre-Alvise Rebuffi
Sylvestre-Alvise Rebuffi
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
10452017
Learning multiple visual domains with residual adapters
SA Rebuffi, H Bilen, A Vedaldi
NIPS 2017, 2017
3182017
Efficient parametrization of multi-domain deep neural networks
SA Rebuffi, H Bilen, A Vedaldi
CVPR 2018, 2018
1812018
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
932016
There and Back Again: Revisiting Backpropagation Saliency Methods
SA Rebuffi, R Fong, X Ji, A Vedaldi
CVPR 2020, 2020
432020
Automatically Discovering and Learning New Visual Categories with Ranking Statistics
K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman
ICLR 2020, 2020
332020
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
292020
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
132021
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
92021
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
22021
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
22021
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
DOING MORE WITH LESS: IMPROVING ROBUSTNESS USING GENERATED DATA
S Gowal, SA Rebuffi, O Wiles, F Stimberg, D Calian, T Mann, L DeepMind
The system can't perform the operation now. Try again later.
Articles 1–14