  Supervised and semi-supervised learning methods have been traditionally
designed for the closed-world setting based on the assumption that unlabeled
test data contains only classes previously encountered in the labeled training
data. However, the real world is inherently open and dynamic, and thus novel,
previously unseen classes may appear in the test data or during the model
deployment. Here, we introduce a new open-world semi-supervised learning
setting in which the model is required to recognize previously seen classes, as
well as to discover novel classes never seen in the labeled dataset. To tackle
the problem, we propose ORCA, an approach that learns to simultaneously
classify and cluster the data. ORCA classifies examples from the unlabeled
dataset to previously seen classes, or forms a novel class by grouping similar
examples together. The key idea in ORCA is in introducing uncertainty based
adaptive margin that effectively circumvents the bias caused by the imbalance
of variance between seen and novel classes/clusters. We demonstrate that ORCA
accurately discovers novel classes and assigns samples to previously seen
classes on benchmark image classification datasets, including CIFAR and
ImageNet. Remarkably, despite solving the harder task ORCA outperforms
semi-supervised methods on seen classes, as well as novel class discovery
methods on novel classes, achieving 7% and 151% improvements on seen and novel
classes in the ImageNet dataset.
