# Real time dense anomaly detection by learning on synthetic negative data

arXiv: https://arxiv.org/abs/2305.15227 · HTML: https://mlsanity.com/paper/2305.15227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 14 of 14)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (2019)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)
- [Why Normalizing Flows Fail to Detect Out-of-Distribution Data](https://mlsanity.com/paper/2006.08545/) (2020)
- [The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation](https://mlsanity.com/paper/1904.03215/) (2019)
- [In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images](https://mlsanity.com/paper/1903.08469/) (2019)
- [Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift](https://mlsanity.com/paper/1908.01098/) (2019)
- [Densely connected normalizing flows](https://mlsanity.com/paper/2106.04627/) (2021)
- [DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition](https://mlsanity.com/paper/2207.02606/) (2022)
- [Dense open-set recognition with synthetic outliers generated by Real NVP](https://mlsanity.com/paper/2011.11094/) (2020)
- [Dense outlier detection and open-set recognition based on training with noisy negative images](https://mlsanity.com/paper/2101.09193/) (2021)
- [Hybrid Open-set Segmentation with Synthetic Negative Data](https://mlsanity.com/paper/2301.08555/) (2023)

## Abstract (arXiv)

Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data. We consider a recent hybrid method that optimizes the same shared representation according to cross-entropy of the discriminative predictions, and negative log likelihood of the predicted energy-based density. We extend that work with a jointly trained generative flow that samples synthetic negatives at the border of the inlier distribution. The proposed extension provides potential to learn the hybrid method without real negative data. Our experiments analyze the impact of training with synthetic negative data and validate contribution of the energy-based density during training and evaluation.