# Taking Class Imbalance Into Account in Open Set Recognition Evaluation

arXiv: https://arxiv.org/abs/2402.06331 · HTML: https://mlsanity.com/paper/2402.06331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [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)
- [Learning Open Set Network with Discriminative Reciprocal Points](https://mlsanity.com/paper/2011.00178/) (2020)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [Understanding Softmax Confidence and Uncertainty](https://mlsanity.com/paper/2106.04972/) (2021)
- [Interpretability and Explainability: A Machine Learning Zoo Mini-tour](https://mlsanity.com/paper/2012.01805/) (2020)
- [Open-Category Classification by Adversarial Sample Generation](https://mlsanity.com/paper/1705.08722/) (2017)
- [Open Set Recognition with Conditional Probabilistic Generative Models](https://mlsanity.com/paper/2008.05129/) (2020)
- [Imbalanced classification: a paradigm-based review](https://mlsanity.com/paper/2002.04592/) (2020)
- [Adversarial Motorial Prototype Framework for Open Set Recognition](https://mlsanity.com/paper/2108.04225/) (2021)
- [torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python](https://mlsanity.com/paper/2305.09646/) (2023)

## Built on by (most cited first, 1 of 1)

- [Synthetic Non-stationary Data Streams for Recognition of the Unknown](https://mlsanity.com/paper/2505.13745/) (2025)

## Abstract (arXiv)

In recent years Deep Neural Network-based systems are not only increasing in popularity but also receive growing user trust. However, due to the closed-world assumption of such systems, they cannot recognize samples from unknown classes and often induce an incorrect label with high confidence. Presented work looks at the evaluation of methods for Open Set Recognition, focusing on the impact of class imbalance, especially in the dichotomy between known and unknown samples. As an outcome of problem analysis, we present a set of guidelines for evaluation of methods in this field.