  Deep learning is pushing the state-of-the-art in many computer vision
applications. However, it relies on large annotated data repositories, and
capturing the unconstrained nature of the real-world data is yet to be solved.
Semi-supervised learning (SSL) complements the annotated training data with a
large corpus of unlabeled data to reduce annotation cost. The standard SSL
approach assumes unlabeled data are from the same distribution as annotated
data. Recently, a more realistic SSL problem, called open-world SSL, is
introduced, where the unannotated data might contain samples from unknown
classes. In this paper, we propose a novel pseudo-label based approach to
tackle SSL in open-world setting. At the core of our method, we utilize sample
uncertainty and incorporate prior knowledge about class distribution to
generate reliable class-distribution-aware pseudo-labels for unlabeled data
belonging to both known and unknown classes. Our extensive experimentation
showcases the effectiveness of our approach on several benchmark datasets,
where it substantially outperforms the existing state-of-the-art on seven
diverse datasets including CIFAR-100 (~17%), ImageNet-100 (~5%), and Tiny
ImageNet (~9%). We also highlight the flexibility of our approach in solving
novel class discovery task, demonstrate its stability in dealing with
imbalanced data, and complement our approach with a technique to estimate the
number of novel classes
