  In open-world learning, an agent starts with a set of known classes, detects,
and manages things that it does not know, and learns them over time from a
non-stationary stream of data. Open-world learning is related to but also
distinct from a multitude of other learning problems and this paper briefly
analyzes the key differences between a wide range of problems including
incremental learning, generalized novelty discovery, and generalized zero-shot
learning. This paper formalizes various open-world learning problems including
open-world learning without labels. These open-world problems can be addressed
with modifications to known elements, we present a new framework that enables
agents to combine various modules for novelty-detection,
novelty-characterization, incremental learning, and instance management to
learn new classes from a stream of unlabeled data in an unsupervised manner,
survey how to adapt a few state-of-the-art techniques to fit the framework and
use them to define seven baselines for performance on the open-world learning
without labels problem. We then discuss open-world learning quality and analyze
how that can improve instance management. We also discuss some of the general
ambiguity issues that occur in open-world learning without labels.
