# Open-Set Recognition with Gaussian Mixture Variational Autoencoders

arXiv: https://arxiv.org/abs/2006.02003 · HTML: https://mlsanity.com/paper/2006.02003/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection](https://mlsanity.com/paper/2308.11480/) (2023)
- [Open-Set Recognition of Breast Cancer Treatments](https://mlsanity.com/paper/2201.02923/) (2022)
- [MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration](https://mlsanity.com/paper/2211.10872/) (2022)
- [UCDSC: Open Set UnCertainty aware Deep Simplex Classifier for Medical Image Datasets](https://mlsanity.com/paper/2511.08196/) (2025)
- [Latent Combinational Game Design](https://mlsanity.com/paper/2206.14203/) (2022)
- [Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition](https://mlsanity.com/paper/2211.13940/) (2022)
- [Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition](https://mlsanity.com/paper/2307.07214/) (2023)
- [Recursive Counterfactual Deconfounding for Object Recognition](https://mlsanity.com/paper/2309.13924/) (2023)
- [OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning](https://mlsanity.com/paper/2310.03848/) (2023)
- [Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition](https://mlsanity.com/paper/2404.10370/) (2024)
- [Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation](https://mlsanity.com/paper/2505.12803/) (2025)
- [Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning](https://mlsanity.com/paper/2508.13005/) (2025)
- [Beyond the Simplex: Balanced Prototype Geometry for Scorer-Agnostic Open-Set Recognition](https://mlsanity.com/paper/2606.01883/) (2026)

## Abstract (arXiv)

In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 improvement of 29.5%, through extensive experiments aided by analytical results.