利用无标签数据实现增广类别的开放集学习
Open-set learning with augmented categories by exploiting unlabelled data
AI总结：
本文首次提出 Open-LACU 策略，统一正例与无标签学习、半监督学习和开放集识别，以利用无标签数据区分已观察与未观察新类别，并构建首个模型取得最先进结果。
摘要：
新类别通常被定义为训练期间未观察到但在测试期间出现的类别。然而，部分标注的训练数据集中可能包含属于新类别的无标签训练样本，这意味着这些类别可能同时出现在训练和测试中。本研究首次在我们所称的已观察新类别与未观察新类别之间进行泛化，并提出一种新的学习策略，称为利用无标签数据实现增广类别的开放集学习（Open-LACU）。在综述现有学习策略之后，我们将 Open-LACU 引入为一种统一策略，融合了正例与无标签学习、半监督学习和开放集识别。随后，我们利用相关研究领域的算法训练过程开发了首个 Open-LACU 模型。所提出的 Open-LACU 分类器取得了最先进且首创性的结果。
英文摘要：
Novel categories are commonly defined as those unobserved during training but present during testing. However, partially labelled training datasets can contain unlabelled training samples that belong to novel categories, meaning these can be present in training and testing. This research is the first to generalise between what we call observed-novel and unobserved-novel categories within a new learning policy called open-set learning with augmented category by exploiting unlabelled data or Open-LACU. After surveying existing learning policies, we introduce Open-LACU as a unified policy of positive and unlabelled learning, semi-supervised learning and open-set recognition. Subsequently, we develop the first Open-LACU model using an algorithmic training process of the relevant research fields. The proposed Open-LACU classifier achieves state-of-the-art and first-of-its-kind results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
