Sylvestre-Alvise Rebuffi
Sylvestre-Alvise Rebuffi
Graduate student at Visual Geometry Group, University of Oxford
Verified email at robots.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
iCaRL: Incremental Classifier and Representation Learning
SA Rebuffi, A Kolesnikov, G Sperl, CH Lampert
CVPR 2017, 2017
8332017
Learning multiple visual domains with residual adapters
SA Rebuffi, H Bilen, A Vedaldi
NIPS 2017, 2017
2712017
Efficient parametrization of multi-domain deep neural networks
SA Rebuffi, H Bilen, A Vedaldi
CVPR 2018, 2018
1592018
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
912016
There and Back Again: Revisiting Backpropagation Saliency Methods
SA Rebuffi, R Fong, X Ji, A Vedaldi
CVPR 2020, 2020
232020
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
182020
Automatically Discovering and Learning New Visual Categories with Ranking Statistics
K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman
ICLR 2020, 2020
172020
Lsd-c: Linearly separable deep clusters
SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman
arXiv preprint arXiv:2006.10039, 2020
52020
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
32021
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
2021
Influence of the input data on learning deep representations
SA Rebuffi
University of Oxford, 2020
2020
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Articles 1–11