Learning to Learn in a Semi-supervised Fashion
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
12363 LNCS
Pages
460-478
Date Issued
2020
Author(s)
Abstract
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.
Subjects
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
SDGs
Type
conference paper