# Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

arXiv: https://arxiv.org/abs/2107.11750 · HTML: https://mlsanity.com/paper/2107.11750/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 25 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [VAE with a VampPrior](https://mlsanity.com/paper/1705.07120/) (2017)
- [Input complexity and out-of-distribution detection with likelihood-based generative models](https://mlsanity.com/paper/1909.11480/) (2019)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Classification Uncertainty of Deep Neural Networks Based on Gradient Information](https://mlsanity.com/paper/1805.08440/) (2018)
- [Out-of-Distribution Detection for Automotive Perception](https://mlsanity.com/paper/2011.01413/) (2020)
- [Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection](https://mlsanity.com/paper/1912.05651/) (2019)
- [Temporal Coherence for Active Learning in Videos](https://mlsanity.com/paper/1908.11757/) (2019)
- [Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE](https://mlsanity.com/paper/2003.08740/) (2020)
- [Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World](https://mlsanity.com/paper/2009.11212/) (2020)
- [Variational auto-encoders with Student's t-prior](https://mlsanity.com/paper/2004.02581/) (2020)
- [LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation](https://mlsanity.com/paper/1709.06019/) (2017)
- [Dirichlet Variational Autoencoder for Text Modeling](https://mlsanity.com/paper/1811.00135/) (2018)
- [HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects](https://mlsanity.com/paper/2011.09654/) (2020)

## Built on by (most cited first, 9 of 9)

- [Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection](https://mlsanity.com/paper/2205.04667/) (2022)
- [Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems](https://mlsanity.com/paper/2207.14694/) (2022)
- [Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios](https://mlsanity.com/paper/2504.13478/) (2025)
- [Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space](https://mlsanity.com/paper/2206.09543/) (2022)
- [CODiT: Conformal Out-of-Distribution Detection in Time-Series Data](https://mlsanity.com/paper/2207.11769/) (2022)
- [Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot](https://mlsanity.com/paper/2211.11520/) (2022)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment](https://mlsanity.com/paper/2409.00880/) (2024)
- [Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding](https://mlsanity.com/paper/2508.16832/) (2025)

## Abstract (arXiv)

Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distribution shifts in the input data between training and test scenarios. Detecting such distribution shifts in real-time is an emerging approach to address the challenge. The high dimensional input space in CPS applications involving imaging adds extra difficulty to the task. Generative learning models are widely adopted for the task, namely out-of-distribution (OoD) detection. To improve the state-of-the-art, we studied existing proposals from both machine learning and CPS fields. In the latter, safety monitoring in real-time for autonomous driving agents has been a focus. Exploiting the spatiotemporal correlation of motion in videos, we can robustly detect hazardous motion around autonomous driving agents. Inspired by the latest advances in the Variational Autoencoder (VAE) theory and practice, we tapped into the prior knowledge in data to further boost OoD detection's robustness. Comparison studies over nuScenes and Synthia data sets show our methods significantly improve detection capabilities of OoD factors unique to driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented. Finally, we customized one proposed method into a twin-encoder model that can be deployed to resource limited embedded devices for real-time OoD detection. Its execution time was reduced over four times in low-precision 8-bit integer inference, while detection capability is comparable to its corresponding floating-point model.