{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:41Z","timestamp":1729225721809,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Previous research in active learning has primarily focused on selecting examples from closed-set data, which consists solely of unlabeled examples from the target classes. However, this approach overlooks the more prevalent scenario of open-set data in real-world applications. Open-set data encompasses examples from both target classes and non-target classes. To fill this gap, we propose a novel framework called JOSAL, which enhances the accuracy of the classifier by precisely selecting the target class examples from open-set data. The JOSAL framework introduces the concept of joint learning, where the Sampler and Classifier components perform sampling and classification tasks, respectively, by sharing example features extracted from a pre-trained Encoder. To maximize the classification accuracy of the Classifier, the framework adopts a novel joint learning strategy. This strategy initially prioritizes optimizing the Sampler and gradually shifts the optimization attention to the Classifier. The experimental results demonstrate that, compared to baselines, our approach exhibits stronger sampling precision and achieves higher classification accuracy. To the best of our knowledge, this is the first work to address the open-set active learning problem using the joint learning paradigm.<\/jats:p>","DOI":"10.3233\/faia240678","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:05:03Z","timestamp":1729170303000},"source":"Crossref","is-referenced-by-count":0,"title":["JOSAL: Joint Learning Framework for Open-Set Active Learning"],"prefix":"10.3233","author":[{"given":"Jun","family":"Xie","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Zhengzhou University, China"}]},{"given":"Xiaohui","family":"Song","sequence":"additional","affiliation":[{"name":"Henan Academy of Sciences, China"}]},{"given":"Yangjie","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Zhengzhou University, China"}]},{"given":"Zhi","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Electro-Communications, Tokyo, Japan"}]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Zhengzhou University, China"}]},{"given":"Hongli","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Zhengzhou University, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240678","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:05:04Z","timestamp":1729170304000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240678","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}