  Category discovery methods aim to find novel categories in unlabeled visual
data. At training time, a set of labeled and unlabeled images are provided,
where the labels correspond to the categories present in the images. The
labeled data provides guidance during training by indicating what types of
visual properties and features are relevant for performing discovery in the
unlabeled data. As a result, changing the categories present in the labeled set
can have a large impact on what is ultimately discovered in the unlabeled set.
Despite its importance, the impact of labeled data selection has not been
explored in the category discovery literature to date. We show that changing
the labeled data can significantly impact discovery performance. Motivated by
this, we propose two new approaches for automatically selecting the most
suitable labeled data based on the similarity between the labeled and unlabeled
data. Our observation is that, unlike in conventional supervised transfer
learning, the best labeled is neither too similar, nor too dissimilar, to the
unlabeled categories. Our resulting approaches obtains state-of-the-art
discovery performance across a range of challenging fine-grained benchmark
datasets.
