# Open Set Recognition with Conditional Probabilistic Generative Models

arXiv: https://arxiv.org/abs/2008.05129 · HTML: https://mlsanity.com/paper/2008.05129/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Counterfactual Zero-Shot and Open-Set Visual Recognition](https://mlsanity.com/paper/2103.00887/) (2021)
- [Class-Specific Semantic Reconstruction for Open Set Recognition](https://mlsanity.com/paper/2207.02158/) (2022)
- [Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning](https://mlsanity.com/paper/2109.05749/) (2021)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [Few-shot Open-set Recognition Using Background as Unknowns](https://mlsanity.com/paper/2207.09059/) (2022)
- [Taking Class Imbalance Into Account in Open Set Recognition Evaluation](https://mlsanity.com/paper/2402.06331/) (2024)
- [Calibrating Class Activation Maps for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2108.12757/) (2021)
- [OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning](https://mlsanity.com/paper/2310.03848/) (2023)
- [Boosting Open Set Recognition Performance through Modulated Representation Learning](https://mlsanity.com/paper/2505.18137/) (2025)

## Abstract (arXiv)

Deep neural networks have made breakthroughs in a wide range of visual understanding tasks. A typical challenge that hinders their real-world applications is that unknown samples may be fed into the system during the testing phase, but traditional deep neural networks will wrongly recognize these unknown samples as one of the known classes. Open set recognition (OSR) is a potential solution to overcome this problem, where the open set classifier should have the flexibility to reject unknown samples and meanwhile maintain high classification accuracy in known classes. Probabilistic generative models, such as Variational Autoencoders (VAE) and Adversarial Autoencoders (AAE), are popular methods to detect unknowns, but they cannot provide discriminative representations for known classification. In this paper, we propose a novel framework, called Conditional Probabilistic Generative Models (CPGM), for open set recognition. The core insight of our work is to add discriminative information into the probabilistic generative models, such that the proposed models can not only detect unknown samples but also classify known classes by forcing different latent features to approximate conditional Gaussian distributions. We discuss many model variants and provide comprehensive experiments to study their characteristics. Experiment results on multiple benchmark datasets reveal that the proposed method significantly outperforms the baselines and achieves new state-of-the-art performance.