  Visual recognition tasks are often limited to dealing with a small subset of
classes simply because the labels for the remaining classes are unavailable. We
are interested in identifying novel concepts in a dataset through
representation learning based on both labeled and unlabeled examples, and
extending the horizon of recognition to both known and novel classes. To
address this challenging task, we propose a combinatorial learning approach,
which naturally clusters the examples in unseen classes using the compositional
knowledge given by multiple supervised meta-classifiers on heterogeneous label
spaces. The representations given by the combinatorial embedding are made more
robust by unsupervised pairwise relation learning. The proposed algorithm
discovers novel concepts via a joint optimization for enhancing the
discrimitiveness of unseen classes as well as learning the representations of
known classes generalizable to novel ones. Our extensive experiments
demonstrate remarkable performance gains by the proposed approach on public
datasets for image retrieval and image categorization with novel class
discovery.
