{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scaling-up-semi-supervised-learning-with","title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","arxiv_id":"2306.01222","date":"2023-06-02","proceeding":null,"authors":["Shuvendu Roy","Ali Etemad"],"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.","url_abs":"https://arxiv.org/abs/2306.01222v2","url_pdf":"https://arxiv.org/pdf/2306.01222v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scaling-up-semi-supervised-learning-with","repo_url":"https://github.com/shuvenduroy/unmixmatch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"network-pruning","task_name":"Network Pruning"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-cifar-10-40-labels","task":"Image Classification","dataset":"CIFAR-10 (40 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuarcy":"52.07"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-34","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10 (250 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"68.72"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-35","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10 (4000 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"89.58"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-37","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 100 Labels (OpenSet, 6/4)","model":"UnMixMatch","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"96.8"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-38","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 400 Labels (OpenSet, 6/4)","model":"UnMixMatch","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"97.2"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-36","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 50 Labels (OpenSet, 6/4)","model":"UnMixMatch","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"95.7"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-33","task":"Semi-Supervised Image Classification","dataset":"CIFAR-100 (10000 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"71.73"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-30","task":"Semi-Supervised Image Classification","dataset":"CIFAR-100 (250 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":2,"of":2,"metrics":{"Accuarcy":"54.18"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-29","task":"Semi-Supervised Image Classification","dataset":"CIFAR-100 (400 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"26.13"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl-5","task":"Semi-Supervised Image Classification","dataset":"STL-10 (1000 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"84.73"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-9","task":"Semi-Supervised Image Classification","dataset":"SVHN (1000 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"91.03"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-7","task":"Semi-Supervised Image Classification","dataset":"SVHN (250 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"80.78"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-8","task":"Semi-Supervised Image Classification","dataset":"SVHN (40 Labels, ImageNet-100 Unlabeled)","model":"UnMixMatch","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"72.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}