This paper concerns open-world classification, where the classifier not only
needs to classify test examples into seen classes that have appeared in
training but also reject examples from unseen or novel classes that have not
appeared in training. Specifically, this paper focuses on discovering the
hidden unseen classes of the rejected examples. Clearly, without prior
knowledge this is difficult. However, we do have the data from the seen
training classes, which can tell us what kind of similarity/difference is
expected for examples from the same class or from different classes. It is
reasonable to assume that this knowledge can be transferred to the rejected
examples and used to discover the hidden unseen classes in them. This paper
aims to solve this problem. It first proposes a joint open classification model
with a sub-model for classifying whether a pair of examples belongs to the same
or different classes. This sub-model can serve as a distance function for
clustering to discover the hidden classes of the rejected examples.
Experimental results show that the proposed model is highly promising.

该论文研究开放世界分类问题，提出一种联合开放分类模型，该模型可以通过远距离测量函数识别未知类别的被拒绝的测试样本，并应用于聚类分析中，从而实现对未知类别的识别。

开放世界分类中的未见类别发现

Unseen Class Discovery in Open-world Classification

Traditional supervised learning makes the closed-world assumption that the
classes appeared in the test data must have appeared in training. This also
applies to text learning or text classification. As learning is used
increasingly in dynamic open environments where some new/test documents may not
belong to any of the training classes, identifying these novel documents during
classification presents an important problem. This problem is called open-world
classification or open classification. This paper proposes a novel deep
learning based approach. It outperforms existing state-of-the-art techniques
dramatically.

本文提出了一种基于深度学习的方法来解决开放分类问题，这种方法在表现上优于现有的现有技术。