iCaRL: Incremental Classifier and Representation Learning. - Sylvestre-Alvise Rebuffi; Alexander Kolesnikov 003; Christoph H. Lampert (CoRR, 2016)

iCaRL: Incremental Classifier and Representation Learning. - Sylvestre-Alvise Rebuffi; Alexander Kolesnikov 003; Georg Sperl; Christoph H. Lampert (CVPR, 2017)

Learning multiple visual domains with residual adapters. - Sylvestre-Alvise Rebuffi; Hakan Bilen; Andrea Vedaldi (NIPS, 2017)

Efficient Parametrization of Multi-Domain Deep Neural Networks. - Sylvestre-Alvise Rebuffi; Hakan Bilen; Andrea Vedaldi (CVPR, 2018)

NormGrad: Finding the Pixels that Matter for Training. - Sylvestre-Alvise Rebuffi; Ruth Fong; Xu Ji; Hakan Bilen; Andrea Vedaldi (CoRR, 2019)

Semi-Supervised Learning with Scarce Annotations. - Sylvestre-Alvise Rebuffi; Sébastien Ehrhardt; Kai Han 001; Andrea Vedaldi; Andrew Zisserman (CVPR Workshops, 2020)

There and Back Again: Revisiting Backpropagation Saliency Methods. - Sylvestre-Alvise Rebuffi; Ruth Fong; Xu Ji; Andrea Vedaldi (CVPR, 2020)

Automatically Discovering and Learning New Visual Categories with Ranking Statistics. - Kai Han 001; Sylvestre-Alvise Rebuffi; Sébastien Ehrhardt; Andrea Vedaldi; Andrew Zisserman (ICLR, 2020)

Influence of the input data on learning deep representations. - Sylvestre-Alvise Rebuffi (2020)

LSD-C: Linearly Separable Deep Clusters. - Sylvestre-Alvise Rebuffi; Sébastien Ehrhardt; Kai Han 001; Andrea Vedaldi; Andrew Zisserman (ICCVW, 2021)

Improving Robustness using Generated Data. - Sven Gowal; Sylvestre-Alvise Rebuffi; Olivia Wiles; Florian Stimberg; Dan Andrei Calian; Timothy A. Mann (NeurIPS, 2021)

Data Augmentation Can Improve Robustness. - Sylvestre-Alvise Rebuffi; Sven Gowal; Dan Andrei Calian; Florian Stimberg; Olivia Wiles; Timothy A. Mann (NeurIPS, 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)

Defending Against Image Corruptions Through Adversarial Augmentations. - Dan Andrei Calian; Florian Stimberg; Olivia Wiles; Sylvestre-Alvise Rebuffi; András György 001; Timothy A. Mann; Sven Gowal (ICLR, 2022)

A Fine-Grained Analysis on Distribution Shift. - Olivia Wiles; Sven Gowal; Florian Stimberg; Sylvestre-Alvise Rebuffi; Ira Ktena; Krishnamurthy Dvijotham; Ali Taylan Cemgil (ICLR, 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 001; Arslan Chaudhry; Xu Owen He; Arthur Douillard; Massimo Caccia; Qixuang Feng; Jiajun Shen; Sylvestre-Alvise Rebuffi; et al. (CoRR, 2022)

AutoNovel: Automatically Discovering and Learning Novel Visual Categories. - Kai Han 001; Sylvestre-Alvise Rebuffi; Sébastien Ehrhardt; Andrea Vedaldi; Andrew Zisserman (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts. - Francesco Croce; Sylvestre-Alvise Rebuffi; Evan Shelhamer; Sven Gowal (CVPR, 2023)

Revisiting adapters with adversarial training. - Sylvestre-Alvise Rebuffi; Francesco Croce; Sven Gowal (ICLR, 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)

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 001; Arslan Chaudhry; Xu Owen He; Arthur Douillard; Massimo Caccia; Qixuan Feng; Jiajun Shen; Sylvestre-Alvise Rebuffi; et al. (J. Mach. Learn. Res., 2023)