# Moving Towards Open Set Incremental Learning: Readily Discovering New Authors

arXiv: https://arxiv.org/abs/1910.12944 · HTML: https://mlsanity.com/paper/1910.12944/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Managing the unknown: a survey on Open Set Recognition and tangential areas](https://mlsanity.com/paper/2312.08785/) (2023)
- [Incremental Deep Neural Network Learning using Classification Confidence Thresholding](https://mlsanity.com/paper/2106.11437/) (2021)
- [Buffer-free Class-Incremental Learning with Out-of-Distribution Detection](https://mlsanity.com/paper/2505.23412/) (2025)
- [Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams](https://mlsanity.com/paper/2411.00876/) (2024)
- [Adapting Feature Attenuation to NLP](https://mlsanity.com/paper/2601.00965/) (2026)

## Abstract (arXiv)

The classification of textual data often yields important information. Most classifiers work in a closed world setting where the classifier is trained on a known corpus, and then it is tested on unseen examples that belong to one of the classes seen during training. Despite the usefulness of this design, often there is a need to classify unseen examples that do not belong to any of the classes on which the classifier was trained. This paper describes the open set scenario where unseen examples from previously unseen classes are handled while testing. This further examines a process of enhanced open set classification with a deep neural network that discovers new classes by clustering the examples identified as belonging to unknown classes, followed by a process of retraining the classifier with newly recognized classes. Through this process the model moves to an incremental learning model where it continuously finds and learns from novel classes of data that have been identified automatically. This paper also develops a new metric that measures multiple attributes of clustering open set data. Multiple experiments across two author attribution data sets demonstrate the creation an incremental model that produces excellent results.