  For open world applications, deep neural networks (DNNs) need to be aware of
previously unseen data and adaptable to evolving environments. Furthermore, it
is desirable to detect and learn novel classes which are not included in the
DNNs underlying set of semantic classes in an unsupervised fashion. The method
proposed in this article builds upon anomaly detection to retrieve
out-of-distribution (OoD) data as candidates for new classes. We thereafter
extend the DNN by $k$ empty classes and fine-tune it on the OoD data samples.
To this end, we introduce two loss functions, which 1) entice the DNN to assign
OoD samples to the empty classes and 2) to minimize the inner-class feature
distances between them. Thus, instead of ground truth which contains labels for
the different novel classes, the DNN obtains a single OoD label together with a
distance matrix, which is computed in advance. We perform several experiments
for image classification and semantic segmentation, which demonstrate that a
DNN can extend its own semantic space by multiple classes without having access
to ground truth.
