  A fundamental limitation of applying semi-supervised learning in real-world
settings is the assumption that unlabeled test data contains only classes
previously encountered in the labeled training data. However, this assumption
rarely holds for data in-the-wild, where instances belonging to novel classes
may appear at testing time. Here, we introduce a novel open-world
semi-supervised learning setting that formalizes the notion that novel classes
may appear in the unlabeled test data. In this novel setting, the goal is to
solve the class distribution mismatch between labeled and unlabeled data, where
at the test time every input instance either needs to be classified into one of
the existing classes or a new unseen class needs to be initialized. To tackle
this challenging problem, we propose ORCA, an end-to-end deep learning approach
that introduces uncertainty adaptive margin mechanism to circumvent the bias
towards seen classes caused by learning discriminative features for seen
classes faster than for the novel classes. In this way, ORCA reduces the gap
between intra-class variance of seen with respect to novel classes. Experiments
on image classification datasets and a single-cell annotation dataset
demonstrate that ORCA consistently outperforms alternative baselines, achieving
25% improvement on seen and 96% improvement on novel classes of the ImageNet
dataset.
