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Sylvestre-Alvise Rebuffi
Sylvestre-Alvise Rebuffi
DeepMind
Adresse e-mail validée de deepmind.com
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Année
iCaRL: Incremental Classifier and Representation Learning
SA Rebuffi, A Kolesnikov, G Sperl, CH Lampert
CVPR 2017, 2017
18872017
Learning multiple visual domains with residual adapters
SA Rebuffi, H Bilen, A Vedaldi
NeurIPS 2017, 2017
5032017
Efficient parametrization of multi-domain deep neural networks
SA Rebuffi, H Bilen, A Vedaldi
CVPR 2018, 2018
2692018
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
1202016
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
912021
There and Back Again: Revisiting Backpropagation Saliency Methods
SA Rebuffi, R Fong, X Ji, A Vedaldi
CVPR 2020, 2020
872020
Automatically discovering and learning new visual categories with ranking statistics
K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman
ICLR 2020, 2020
812020
Improving Robustness using Generated Data
S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, T Mann
NeurIPS 2021, 2021
612021
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
532021
Data Augmentation Can Improve Robustness
SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann
NeurIPS 2021, 2021
482021
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
412020
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
232021
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
212021
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
162021
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, ...
arXiv preprint arXiv:2211.11747, 2022
2022
Revisiting adapters with adversarial training
SA Rebuffi, F Croce, S Gowal
arXiv preprint arXiv:2210.04886, 2022
2022
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
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