Title: Mutual Information-guided Knowledge Transfer for Open-World Semi-Supervised Learning. Abstract: We tackle the open-world semi-supervised learning problem, aiming to cluster novel classes and classify seen classes in unlabeled data based on labeled data from seen classes. The main challenge is to transfer knowledge contained in seen class data to unseen ones. Previous methods mostly transfer knowledge through sharing representation space. However, they learn the seen and unseen classes classifier in a disjoint manner, neglecting the underlying relation between predictions on the seen and unseen classes. Therefore, the learned representations and classifiers are less effective for clustering unseen classes. In this paper, we propose a novel and general method to transfer knowledge between seen and unseen classes. Our insight is to utilize mutual information to measure the generic statistical dependency between seen and unseen classes in the classifier output space, which couple the learning of classifier and promote transferring knowledge between two data sets. To validate the effectiveness and generalization of our method, we conduct extensive experiments on several benchmarks, including CIFAR10/100, Imagenet100, Oxford-IIIT Pet and FGVC-Aicraft datasets. Our results show that the proposed method outperforms previous SOTA by a significant margin on almost all benchmarks.
