{"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/lidam-semi-supervised-learning-with-localized","title":"LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching","arxiv_id":"2010.06668","date":"2020-10-13","proceeding":null,"authors":["Qun Liu","Matthew Shreve","Raja Bala"],"abstract":"Although data is abundant, data labeling is expensive. Semi-supervised learning methods combine a few labeled samples with a large corpus of unlabeled data to effectively train models. This paper introduces our proposed method LiDAM, a semi-supervised learning approach rooted in both domain adaptation and self-paced learning. LiDAM first performs localized domain shifts to extract better domain-invariant features for the model that results in more accurate clusters and pseudo-labels. These pseudo-labels are then aligned with real class labels in a self-paced fashion using a novel iterative matching technique that is based on majority consistency over high-confidence predictions. Simultaneously, a final classifier is trained to predict ground-truth labels until convergence. LiDAM achieves state-of-the-art performance on the CIFAR-100 dataset, outperforming FixMatch (73.50% vs. 71.82%) when using 2500 labels.","url_abs":"https://arxiv.org/abs/2010.06668v2","url_pdf":"https://arxiv.org/pdf/2010.06668v2.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":[],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"}],"methods":[{"method_slug":"fixmatch","method_name":"FixMatch"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-11","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 1000 Labels","model":"LiDAM","rank_in_archive_order":4,"of":9,"metrics":{"Accuracy":"89.04"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-6","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 250 Labels","model":"LiDAM","rank_in_archive_order":23,"of":27,"metrics":{"Percentage error":"19.17"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 4000 Labels","model":"LiDAM","rank_in_archive_order":36,"of":49,"metrics":{"Percentage error":"7.48"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-9","task":"Semi-Supervised Image Classification","dataset":"CIFAR-100, 2500 Labels","model":"LiDAM","rank_in_archive_order":11,"of":16,"metrics":{"Percentage error":"26.50"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-24","task":"Semi-Supervised Image Classification","dataset":"CIFAR-100, 5000 Labels","model":"LiDAM","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (%)":"75.14"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-4","task":"Semi-Supervised Image Classification","dataset":"CIFAR-100, 5000Labels","model":"LiDAM","rank_in_archive_order":1,"of":2,"metrics":{"Percentage correct":"75.14"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-2","task":"Semi-Supervised Image Classification","dataset":"cifar-100, 10000 Labels","model":"LiDAM","rank_in_archive_order":20,"of":29,"metrics":{"Percentage error":"23.22"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}