Sylvestre-Alvise Rebuffi

According to our database, Sylvestre-Alvise Rebuffi authored at least 20 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Generative models improve fairness of medical classifiers under distribution shifts.

Ira Ktena, Olivia Wiles, Isabela Albuquerque, Sylvestre-Alvise Rebuffi, Ryutaro Tanno, Abhijit Guha Roy, Shekoofeh Azizi, Danielle Belgrave, Pushmeet Kohli, Alan Karthikesalingam, A. Taylan Cemgil, Sven Gowal

CoRR, 2023

Revisiting adapters with adversarial training.

Sylvestre-Alvise Rebuffi, Francesco Croce, Sven Gowal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts.

Francesco Croce, Sylvestre-Alvise Rebuffi, Evan Shelhamer, Sven Gowal

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

AutoNovel: Automatically Discovering and Learning Novel Visual Categories.

Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman

IEEE Trans. Pattern Anal. Mach. Intell., 2022

NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research.

Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuang Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato

CoRR, 2022

A Fine-Grained Analysis on Distribution Shift.

Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, Ali Taylan Cemgil

Proceedings of the Tenth International Conference on Learning Representations, 2022

Defending Against Image Corruptions Through Adversarial Augmentations.

Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A. Mann, Sven Gowal

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Fixing Data Augmentation to Improve Adversarial Robustness.

Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian, Florian Stimberg, Olivia Wiles, Timothy A. Mann

CoRR, 2021

Data Augmentation Can Improve Robustness.

Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Timothy A. Mann

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improving Robustness using Generated Data.

Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, Timothy A. Mann

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

LSD-C: Linearly Separable Deep Clusters.

Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

2020

Influence of the input data on learning deep representations.

Sylvestre-Alvise Rebuffi

PhD thesis, 2020

Automatically Discovering and Learning New Visual Categories with Ranking Statistics.

Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman

Proceedings of the 8th International Conference on Learning Representations, 2020

There and Back Again: Revisiting Backpropagation Saliency Methods.

Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Semi-Supervised Learning with Scarce Annotations.

Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

NormGrad: Finding the Pixels that Matter for Training.

Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Hakan Bilen, Andrea Vedaldi

CoRR, 2019

2018

Efficient Parametrization of Multi-Domain Deep Neural Networks.

Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Learning multiple visual domains with residual adapters.

Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

iCaRL: Incremental Classifier and Representation Learning.

Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, Christoph H. Lampert

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

iCaRL: Incremental Classifier and Representation Learning.

Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Christoph H. Lampert

CoRR, 2016