OPEN-WORLD SEMI-SUPERVISED LEARNING

Abstract

Supervised and semi-supervised learning methods have been traditionally designed for the closed-world setting which is based on the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, the real world is often 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 jointly learns a feature representation and a classifier on the labeled and unlabeled subsets of the data. 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. We demonstrate that ORCA accurately discovers novel classes and assigns samples to previously seen classes on standard 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 unseen classes, achieving 7% and 151% improvements on seen and unseen classes of the ImageNet dataset.

1. INTRODUCTION

With the advent of deep learning, remarkable breakthroughs have been achieved and current machine learning systems excel on tasks with large quantities of labeled data. Despite the strengths, the vast majority of models are designed for the closed-world setting rooted in the assumption that training and test data come from the same set of predefined classes. This assumption, however, rarely holds in practice, as labeling data depends on the domain-specific knowledge which can be severely incomplete and insufficient to account for all possible scenarios. Thus, it is unrealistic to expect that one can identify and prelabel all categories/classes ahead of time, and manually supervise machine learning models. In contrast to the commonly assumed closed world, the real world is inherently dynamic and open -new classes can emerge in the test data that have never been encountered during training. Openworld setting requires the models to be able to classify previously seen classes, but also effectively handle never-before-seen classes. This task is very natural to human intelligence; children can effortlessly recognize previously learnt concepts, but also detect the patterns and differences of the new ones. However, it is still an open question whether we can design versatile models that can successfully deal with the world of unknown, while not forgetting the world of known. Semi-supervised learning (SSL) (Chapelle et al., 2009) aims in leveraging unlabeled data when labels are difficult and costly to obtain. Recent works (Oliver et al., 2018; Chen et al., 2020b) show that incorporating novel classes in the unlabeled set degrades performance of SSL methods. To alleviate this limitation, Guo et al. (2020) ensure safety of SSL with the presence of novel classes as well. However, the ability to differentiate between seen and unseen classes is not sufficient as we need methods that can properly handle unseen classes. On the other hand, methods for discovering novel classes (Hsu et al., 2018; 2019; Han et al., 2019; 2020) utilize labeled data solely to learn a richer representation, and are not able to recognize seen and discover unseen classes at the same time.

