{"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/single-shot-active-learning-using-pseudo","title":"Single Shot Active Learning using Pseudo Annotators","arxiv_id":"1805.06660","date":"2018-05-17","proceeding":null,"authors":["Yazhou Yang","Marco Loog"],"abstract":"Standard myopic active learning assumes that human annotations are always\nobtainable whenever new samples are selected. This, however, is unrealistic in\nmany real-world applications where human experts are not readily available at\nall times. In this paper, we consider the single shot setting: all the required\nsamples should be chosen in a single shot and no human annotation can be\nexploited during the selection process. We propose a new method, Active\nLearning through Random Labeling (ALRL), which substitutes single human\nannotator for multiple, what we will refer to as, pseudo annotators. These\npseudo annotators always provide uniform and random labels whenever new\nunlabeled samples are queried. This random labeling enables standard active\nlearning algorithms to also exhibit the exploratory behavior needed for single\nshot active learning. The exploratory behavior is further enhanced by selecting\nthe most representative sample via minimizing nearest neighbor distance between\nunlabeled samples and queried samples. Experiments on real-world datasets\ndemonstrate that the proposed method outperforms several state-of-the-art\napproaches.","url_abs":"http://arxiv.org/abs/1805.06660v1","url_pdf":"http://arxiv.org/pdf/1805.06660v1.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":"single-shot-active-learning-using-pseudo","repo_url":"https://github.com/YazhouTUD/single_shot_AL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.06660","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}