Rebuffi, Sylvestre-Alvise

iCaRL: Incremental Classifier and Representation Learning

Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 3,988

Learning multiple visual domains with residual adapters

Rebuffi, S.-A., Bilen, H., & Vedaldi, A. (2017) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 605

Generative models improve fairness of medical classifiers under distribution shifts

Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., Karthikesalingam, A., et al. (2023) · Nature Medicine

Artificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

Cited by 165

Generative models improve fairness of medical classifiers under distribution shifts

Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A. G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

Cited by 165