# One Versus all for deep Neural Network Incertitude (OVNNI) quantification

arXiv: https://arxiv.org/abs/2006.00954 · HTML: https://mlsanity.com/paper/2006.00954/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [RbA: Segmenting Unknown Regions Rejected by All](https://mlsanity.com/paper/2211.14293/) (2022)
- [DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition](https://mlsanity.com/paper/2207.02606/) (2022)
- [Maskomaly:Zero-Shot Mask Anomaly Segmentation](https://mlsanity.com/paper/2305.16972/) (2023)
- [Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation](https://mlsanity.com/paper/2309.04573/) (2023)
- [Dense outlier detection and open-set recognition based on training with noisy negative images](https://mlsanity.com/paper/2101.09193/) (2021)
- [UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs](https://mlsanity.com/paper/2201.13279/) (2022)
- [Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data](https://mlsanity.com/paper/2112.12833/) (2021)
- [Improving CLIP Robustness with Knowledge Distillation and Self-Training](https://mlsanity.com/paper/2309.10361/) (2023)
- [Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation](https://mlsanity.com/paper/2104.13756/) (2021)
- [Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging](https://mlsanity.com/paper/2201.07199/) (2022)
- [UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers](https://mlsanity.com/paper/2402.09264/) (2024)
- [Probing Contextual Diversity for Dense Out-of-Distribution Detection](https://mlsanity.com/paper/2208.14195/) (2022)
- [On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression](https://mlsanity.com/paper/2202.12369/) (2022)
- [SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier](https://mlsanity.com/paper/2206.13923/) (2022)
- [Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes](https://mlsanity.com/paper/2302.06815/) (2023)
- [Distributional Gaussian Processes Layers for Out-of-Distribution Detection](https://mlsanity.com/paper/2206.13346/) (2022)
- [Learning a Discriminant Latent Space with Neural Discriminant Analysis](https://mlsanity.com/paper/2107.06209/) (2021)

## Abstract (arXiv)

Deep neural networks (DNNs) are powerful learning models yet their results are not always reliable. This is due to the fact that modern DNNs are usually uncalibrated and we cannot characterize their epistemic uncertainty. In this work, we propose a new technique to quantify the epistemic uncertainty of data easily. This method consists in mixing the predictions of an ensemble of DNNs trained to classify One class vs All the other classes (OVA) with predictions from a standard DNN trained to perform All vs All (AVA) classification. On the one hand, the adjustment provided by the AVA DNN to the score of the base classifiers allows for a more fine-grained inter-class separation. On the other hand, the two types of classifiers enforce mutually their detection of out-of-distribution (OOD) samples, circumventing entirely the requirement of using such samples during training. Our method achieves state of the art performance in quantifying OOD data across multiple datasets and architectures while requiring little hyper-parameter tuning.