  In this paper, we tackle the problem of novel visual category discovery,
i.e., grouping unlabelled images from new classes into different semantic
partitions by leveraging a labelled dataset that contains images from other
different but relevant categories. This is a more realistic and challenging
setting than conventional semi-supervised learning. We propose a two-branch
learning framework for this problem, with one branch focusing on local
part-level information and the other branch focusing on overall
characteristics. To transfer knowledge from the labelled data to the
unlabelled, we propose using dual ranking statistics on both branches to
generate pseudo labels for training on the unlabelled data. We further
introduce a mutual knowledge distillation method to allow information exchange
and encourage agreement between the two branches for discovering new
categories, allowing our model to enjoy the benefits of global and local
features. We comprehensively evaluate our method on public benchmarks for
generic object classification, as well as the more challenging datasets for
fine-grained visual recognition, achieving state-of-the-art performance.
