论文标题
学习以半监督的方式学习
Learning to Learn in a Semi-Supervised Fashion
论文作者
论文摘要
为了从标记和未标记的数据中解决半监督的学习，我们提出了一种新型的元学习方案。我们特别认为，标记和未标记的数据共享不相交的地面真相标签集，可以看到该任务，例如重新识别或图像检索。我们的学习计划利用了从标记到未标记数据的信息的想法。我们没有像大多数元学习算法那样拟合相关的类相似性得分，而是建议从标记的数据中得出面向语义的相似性表示，并将此类表示形式传输到未标记的表示。因此，我们的策略可以被视为一种自学的学习计划，可以应用于完全监督的学习任务，以提高绩效。我们对各种任务和设置的实验证实了我们提出的方法的有效性及其优越性，对最先进的方法的优势。
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.
