# Open-world Machine Learning: Applications, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2105.13448 · HTML: https://mlsanity.com/paper/2105.13448/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 218 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 30 of 63)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [Overcoming Catastrophic Forgetting by Incremental Moment Matching](https://mlsanity.com/paper/1703.08475/) (2017)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Generative OpenMax for Multi-Class Open Set Classification](https://mlsanity.com/paper/1707.07418/) (2017)
- [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018)

## Built on by (most cited first, 30 of 34)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [MCU: An Evaluation Framework for Open-Ended Game Agents](https://mlsanity.com/paper/2310.08367/) (2023)
- [AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation](https://mlsanity.com/paper/2203.08994/) (2022)
- [Open-World Continual Learning: Unifying Novelty Detection and Continual Learning](https://mlsanity.com/paper/2304.10038/) (2023)
- [Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data](https://mlsanity.com/paper/2212.09171/) (2022)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights](https://mlsanity.com/paper/2303.07557/) (2023)
- [Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts](https://mlsanity.com/paper/2303.05155/) (2023)
- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [Self-Initiated Open World Learning for Autonomous AI Agents](https://mlsanity.com/paper/2110.11385/) (2021)
- [Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness](https://mlsanity.com/paper/2302.10253/) (2023)
- [Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and Techniques](https://mlsanity.com/paper/2304.12583/) (2023)
- [Managing the unknown: a survey on Open Set Recognition and tangential areas](https://mlsanity.com/paper/2312.08785/) (2023)
- [Realistic Evaluation of TabPFN v2 in Open Environments](https://mlsanity.com/paper/2505.16226/) (2025)
- [A Contrastive Symmetric Forward-Forward Algorithm (SFFA) for Continual Learning Tasks](https://mlsanity.com/paper/2409.07387/) (2024)
- [CL-VISTA: Benchmarking Continual Learning in Video Large Language Models](https://mlsanity.com/paper/2604.00677/) (2026)
- [Open World Learning Graph Convolution for Latency Estimation in Routing Networks](https://mlsanity.com/paper/2207.14643/) (2022)
- [Detecting Novelties with Empty Classes](https://mlsanity.com/paper/2305.00983/) (2023)
- [General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance](https://mlsanity.com/paper/2307.14283/) (2023)
- [Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes](https://mlsanity.com/paper/2310.13723/) (2023)
- [A Metalearned Neural Circuit for Nonparametric Bayesian Inference](https://mlsanity.com/paper/2311.14601/) (2023)
- [LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System](https://mlsanity.com/paper/2402.05130/) (2024)
- [Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning](https://mlsanity.com/paper/2408.10046/) (2024)
- [Uncertainty Quantification in Continual Open-World Learning](https://mlsanity.com/paper/2412.16409/) (2024)
- [Temporally Consistent Unsupervised Segmentation for Mobile Robot Perception](https://mlsanity.com/paper/2507.22194/) (2025)
- [POWN: Prototypical Open-World Node Classification](https://mlsanity.com/paper/2406.09926/) (2024)
- [Exploring the Tradeoff Between Diversity and Discrimination for Continuous Category Discovery](https://mlsanity.com/paper/2508.11173/) (2025)
- [Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction](https://mlsanity.com/paper/2510.15422/) (2025)

## Abstract (arXiv)

Traditional machine learning mainly supervised learning, follows the assumptions of closed-world learning, i.e., for each testing class, a training class is available. However, such machine learning models fail to identify the classes which were not available during training time. These classes can be referred to as unseen classes. Whereas open-world machine learning (OWML) deals with unseen classes. In this paper, first, we present an overview of OWML with importance to the real-world context. Next, different dimensions of open-world machine learning are explored and discussed. The area of OWML gained the attention of the research community in the last decade only. We have searched through different online digital libraries and scrutinized the work done in the last decade. This paper presents a systematic review of various techniques for OWML. It also presents the research gaps, challenges, and future directions in open-world machine learning. This paper will help researchers understand the comprehensive developments of OWML and the likelihood of extending the research in suitable areas. It will also help to select applicable methodologies and datasets to explore this further.