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- W4379471612 abstract "We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained unlabelled data in order to scale up performance. Most existing semi-supervised methods rely on the assumption that labelled and unlabelled samples are drawn from the same distribution, which limits the potential for improvement through the use of free-living unlabeled data. Consequently, the generalizability and scalability of semi-supervised learning are often hindered by this assumption. Our method aims to overcome these constraints and effectively utilize unconstrained unlabelled data in semi-supervised learning. UnMixMatch consists of three main components: a supervised learner with hard augmentations that provides strong regularization, a contrastive consistency regularizer to learn underlying representations from the unlabelled data, and a self-supervised loss to enhance the representations that are learnt from the unlabelled data. We perform extensive experiments on 4 commonly used datasets and demonstrate superior performance over existing semi-supervised methods with a performance boost of 4.79%. Extensive ablation and sensitivity studies show the effectiveness and impact of each of the proposed components of our method." @default.
- W4379471612 created "2023-06-07" @default.
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- W4379471612 creator A5042076292 @default.
- W4379471612 date "2023-06-01" @default.
- W4379471612 modified "2023-10-01" @default.
- W4379471612 title "Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data" @default.
- W4379471612 doi "https://doi.org/10.48550/arxiv.2306.01222" @default.
- W4379471612 hasPublicationYear "2023" @default.
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