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Contrastive Learning for Online Semi-Supervised General Continual Learning

2022-07-12 15:32:00
Nicolas Michel, Romain Negrel, Giovanni Chierchia, Jean-François Bercher

Abstract

We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data. We demonstrate its efficiency by devising a memory-based method trained on an unlabeled data stream, where every data added to memory is labeled using an oracle. Our approach outperforms existing semi-supervised methods when few labels are available, and obtain similar results to state-of-the-art supervised methods while using only 2.6% of labels on Split-CIFAR10 and 10% of labels on Split-CIFAR100.

Abstract (translated)

URL

https://arxiv.org/abs/2207.05615

PDF

https://arxiv.org/pdf/2207.05615.pdf


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