# Autoencoder-based Semantic Novelty Detection: Towards Dependable AI-based Systems

arXiv: https://arxiv.org/abs/2108.10851 · HTML: https://mlsanity.com/paper/2108.10851/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Autonomics: In Search of a Foundation for Next Generation Autonomous Systems](https://mlsanity.com/paper/1911.07133/) (2019)

## Built on by (most cited first, 4 of 4)

- [Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring](https://mlsanity.com/paper/2207.05078/) (2022)
- [A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classes](https://mlsanity.com/paper/2504.12991/) (2025)
- [Towards exploring adversarial learning for anomaly detection in complex driving scenes](https://mlsanity.com/paper/2307.05256/) (2023)
- [A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System](https://mlsanity.com/paper/2412.16762/) (2024)

## Abstract (arXiv)

Many autonomous systems, such as driverless taxis, perform safety critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for the environment perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection - identifying data that differ in some respect from the data used for training - becomes a safety measure for system development and operation. In this paper, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from literature by minimizing false negatives.