https://scholars.lib.ntu.edu.tw/handle/123456789/581254
標題: | Learning to Learn in a Semi-supervised Fashion | 作者: | Chen Y.-C Chou C.-T Wang Y.-C.F. YU-CHIANG WANG |
關鍵字: | Computer vision; Data Sharing; Semantics; Semi-supervised learning; Labeled and unlabeled data; Learning schemes; Learning to learn; Person re identifications; Semi-supervised; Similarity representation; Similarity scores; State-of-the-art methods; Learning algorithms | 公開日期: | 2020 | 卷: | 12363 LNCS | 起(迄)頁: | 460-478 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval. Our learning scheme exploits the idea of leveraging information from labeled to unlabeled data. Instead of fitting the associated class-wise similarity scores as most meta-learning algorithms do, we propose to derive semantics-oriented similarity representations from labeled data, and transfer such representation to unlabeled ones. Thus, our strategy can be viewed as a self-supervised learning scheme, which can be applied to fully supervised learning tasks for improved performance. Our experiments on various tasks and settings confirm the effectiveness of our proposed approach and its superiority over the state-of-the-art methods. ? 2020, Springer Nature Switzerland AG. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097806226&doi=10.1007%2f978-3-030-58523-5_27&partnerID=40&md5=2584097a398d02471762a03fa2fdc3fb https://scholars.lib.ntu.edu.tw/handle/123456789/581254 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-58523-5_27 |
顯示於: | 電機工程學系 |
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