Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data

• URL: http://arxiv.org/abs/2306.01222v2
• Date: Sat, 13 Jan 2024 02:01:31 GMT
• Title: Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
• Authors: Shuvendu Roy, Ali Etemad
• Abstract summary: We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained 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%.
• Score: 27.75143621836449
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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