# Dense open-set recognition with synthetic outliers generated by Real NVP

arXiv: https://arxiv.org/abs/2011.11094 · HTML: https://mlsanity.com/paper/2011.11094/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 29 of 29)

- [VOS: Learning What You Don't Know by Virtual Outlier Synthesis](https://mlsanity.com/paper/2202.01197/) (2022)
- [Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation](https://mlsanity.com/paper/2107.11264/) (2021)
- [Unmasking Anomalies in Road-Scene Segmentation](https://mlsanity.com/paper/2307.13316/) (2023)
- [DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition](https://mlsanity.com/paper/2207.02606/) (2022)
- [On Advantages of Mask-level Recognition for Outlier-aware Segmentation](https://mlsanity.com/paper/2301.03407/) (2023)
- [Maskomaly:Zero-Shot Mask Anomaly Segmentation](https://mlsanity.com/paper/2305.16972/) (2023)
- [Outlier detection by ensembling uncertainty with negative objectness](https://mlsanity.com/paper/2402.15374/) (2024)
- [Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects](https://mlsanity.com/paper/2210.02074/) (2022)
- [Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation](https://mlsanity.com/paper/2309.04573/) (2023)
- [UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs](https://mlsanity.com/paper/2201.13279/) (2022)
- [Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.02790/) (2023)
- [Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection](https://mlsanity.com/paper/2302.07106/) (2023)
- [DOS: Diverse Outlier Sampling for Out-of-Distribution Detection](https://mlsanity.com/paper/2306.02031/) (2023)
- [AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2405.07865/) (2024)
- [Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data](https://mlsanity.com/paper/2112.12833/) (2021)
- [Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving](https://mlsanity.com/paper/2505.02148/) (2025)
- [OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection](https://mlsanity.com/paper/2411.17761/) (2024)
- [Hybrid Open-set Segmentation with Synthetic Negative Data](https://mlsanity.com/paper/2301.08555/) (2023)
- [AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains](https://mlsanity.com/paper/2212.14397/) (2022)
- [Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection](https://mlsanity.com/paper/2501.16718/) (2025)
- [Probing Contextual Diversity for Dense Out-of-Distribution Detection](https://mlsanity.com/paper/2208.14195/) (2022)
- [Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes](https://mlsanity.com/paper/2302.06815/) (2023)
- [Learning to Generate Training Datasets for Robust Semantic Segmentation](https://mlsanity.com/paper/2308.02535/) (2023)
- [Road Obstacle Video Segmentation](https://mlsanity.com/paper/2509.13181/) (2025)
- [Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune](https://mlsanity.com/paper/2402.16911/) (2024)
- [Real time dense anomaly detection by learning on synthetic negative data](https://mlsanity.com/paper/2305.15227/) (2023)
- [Robust Uncertainty Estimation for Classification of Maritime Objects](https://mlsanity.com/paper/2307.01325/) (2023)
- [What Can We Learn from Harry Potter? An Exploratory Study of Visual Representation Learning from Atypical Videos](https://mlsanity.com/paper/2508.21770/) (2025)
- [SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving](https://mlsanity.com/paper/2604.08008/) (2026)

## Abstract (arXiv)

Today's deep models are often unable to detect inputs which do not belong to the training distribution. This gives rise to confident incorrect predictions which could lead to devastating consequences in many important application fields such as healthcare and autonomous driving. Interestingly, both discriminative and generative models appear to be equally affected. Consequently, this vulnerability represents an important research challenge. We consider an outlier detection approach based on discriminative training with jointly learned synthetic outliers. We obtain the synthetic outliers by sampling an RNVP model which is jointly trained to generate datapoints at the border of the training distribution. We show that this approach can be adapted for simultaneous semantic segmentation and dense outlier detection. We present image classification experiments on CIFAR-10, as well as semantic segmentation experiments on three existing datasets (StreetHazards, WD-Pascal, Fishyscapes Lost & Found), and one contributed dataset. Our models perform competitively with respect to the state of the art despite producing predictions with only one forward pass.