  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.
