论文标题
元问题网络：在开放式积极学习中解决纯度信息的困境
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
论文作者
论文摘要
等待注释的未标记的数据示例不可避免地包含开放式噪声。一些积极的学习研究试图通过滤除嘈杂的例子来处理这种开放式噪声以进行样本选择。但是，由于关注查询集中的示例的纯度会导致忽略示例的信息，因此纯度和信息性的最佳平衡仍然是一个重要问题。在本文中，为了解决开放式积极学习中的这种纯度信息难题，我们提出了一种新型的元网络，（MQ-NET），可以适应地发现这两个因素之间的最佳平衡。具体而言，通过利用Active学习的多轮属性，我们使用无需其他验证集的查询集训练MQ-NET。此外，MQ-NET通过新颖的天际线正则化有效地捕获了未标记的例子之间的明显优势关系。与最新方法相比，对多个开放式主动学习方案进行了广泛的实验表明，所提出的MQ-NET在准确性方面取得了20.14％的提高。
Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection by filtering out the noisy examples. However, because focusing on the purity of examples in a query set leads to overlooking the informativeness of the examples, the best balancing of purity and informativeness remains an important question. In this paper, to solve this purity-informativeness dilemma in open-set active learning, we propose a novel Meta-Query-Net,(MQ-Net) that adaptively finds the best balancing between the two factors. Specifically, by leveraging the multi-round property of active learning, we train MQ-Net using a query set without an additional validation set. Furthermore, a clear dominance relationship between unlabeled examples is effectively captured by MQ-Net through a novel skyline regularization. Extensive experiments on multiple open-set active learning scenarios demonstrate that the proposed MQ-Net achieves 20.14% improvement in terms of accuracy, compared with the state-of-the-art methods.
