# Extreme Value Theory for Open Set Classification -- GPD and GEV Classifiers

arXiv: https://arxiv.org/abs/1808.09902 · HTML: https://mlsanity.com/paper/1808.09902/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

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

- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [LORD: Leveraging Open-Set Recognition with Unknown Data](https://mlsanity.com/paper/2308.12584/) (2023)
- [UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets](https://mlsanity.com/paper/2511.08196/) (2025)
- [Incrementally Zero-Shot Detection by an Extreme Value Analyzer](https://mlsanity.com/paper/2103.12609/) (2021)
- [Learning without Seeing nor Knowing: Towards Open Zero-Shot Learning](https://mlsanity.com/paper/2103.12437/) (2021)
- [Boosting Open Set Recognition Performance through Modulated Representation Learning](https://mlsanity.com/paper/2505.18137/) (2025)

## Abstract (arXiv)

Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresses this problem and achieves competitive performance in specific cases. We show that this algorithm can fail when the geometries of known and unknown classes differ. To overcome this problem, we propose two new algorithms relying on approximations from extreme value theory. We show the effectiveness of our classifiers in simulations and on the LETTER and MNIST data sets.