# A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

arXiv: https://arxiv.org/abs/2011.12906 · HTML: https://mlsanity.com/paper/2011.12906/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Open-World Continual Learning: Unifying Novelty Detection and Continual Learning](https://mlsanity.com/paper/2304.10038/) (2023)
- [Continual Learning for Autonomous Robots: A Prototype-based Approach](https://mlsanity.com/paper/2404.00418/) (2024)
- [NovPhy: A Testbed for Physical Reasoning in Open-world Environments](https://mlsanity.com/paper/2303.01711/) (2023)
- [Rapid Open-World Adaptation by Adaptation Principles Learning](https://mlsanity.com/paper/2312.11138/) (2023)
- [Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction](https://mlsanity.com/paper/2510.15422/) (2025)
- [Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network](https://mlsanity.com/paper/2511.01553/) (2025)

## Abstract (arXiv)

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.