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.

翻译：新类别通常定义为在训练中未出现但在测试中存在的类别。然而，部分标注的训练数据集可能包含属于新类别的未标注训练样本，这意味着这些新类别可能在训练和测试阶段均存在。本研究首次在一种名为"利用未标注数据实现带增广类别的开放集学习"（Open-LACU）的新学习范式下，对所谓的"观测新类别"与"非观测新类别"进行了广义区分。在综述现有学习范式后，我们将Open-LACU定义为正类与未标注学习、半监督学习以及开放集识别的统一范式。随后，我们基于相关研究领域的算法训练流程，构建了首个Open-LACU模型。所提出的Open-LACU分类器取得了当前最优且具有开创性的实验结果。