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arXiv · 2207.05615

Contrastive Learning for Online Semi-Supervised General Continual Learning

Nicolas Michel·Romain Negrel·Giovanni Chierchia·Jean-François Bercher
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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.

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Nicolas Michel, Romain Negrel, Giovanni Chierchia, Jean-François Bercher. 2022-07-12. Contrastive Learning for Online Semi-Supervised General Continual Learning. https://arxiv.org/abs/2207.05615

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