Traditional semi-supervised learning tasks assume that both labeled and
unlabeled data follow the same class distribution, but the realistic open-world
scenarios are of more complexity with unknown novel classes mixed in the
unlabeled set. Therefore, it is of great challenge to not only recognize
samples from known classes but also discover the unknown number of novel
classes within the unlabeled data. In this paper, we introduce a new open-world
semi-supervised novel class discovery approach named OpenNCD, a progressive
bi-level contrastive learning method over multiple prototypes. The proposed
method is composed of two reciprocally enhanced parts. First, a bi-level
contrastive learning method is introduced, which maintains the pair-wise
similarity of the prototypes and the prototype group levels for better
representation learning. Then, a reliable prototype similarity metric is
proposed based on the common representing instances. Prototypes with high
similarities will be grouped progressively for known class recognition and
novel class discovery. Extensive experiments on three image datasets are
conducted and the results show the effectiveness of the proposed method in
open-world scenarios, especially with scarce known classes and labels.

该文提出了一种名为 OpenNCD 的新的半监督开放世界的未知类别发现方法，它是一种在多个原型上进行的渐进式双层对比学习方法，该方法由两个相互增强的部分组成。首先，介绍了一种双层对比学习方法，然后提出了一个可靠的原型相似度度量方法。对三个图像数据集进行了广泛的实验，结果表明该方法在开放世界的情况下特别有效，特别是在已知类别和标签稀缺的情况下。