  We consider the problem of discovering novel object categories in an image
collection. While these images are unlabelled, we also assume prior knowledge
of related but different image classes. We use such prior knowledge to reduce
the ambiguity of clustering, and improve the quality of the newly discovered
classes. Our contributions are twofold. The first contribution is to extend
Deep Embedded Clustering to a transfer learning setting; we also improve the
algorithm by introducing a representation bottleneck, temporal ensembling, and
consistency. The second contribution is a method to estimate the number of
classes in the unlabelled data. This also transfers knowledge from the known
classes, using them as probes to diagnose different choices for the number of
classes in the unlabelled subset. We thoroughly evaluate our method,
substantially outperforming state-of-the-art techniques in a large number of
benchmarks, including ImageNet, OmniGlot, CIFAR-100, CIFAR-10, and SVHN.
