Autoencoder-based Semantic Novelty Detection: Towards Dependable AI-based Systems
This addresses safety concerns in autonomous systems like driverless taxis by improving novelty detection, though it is incremental as it builds on existing autoencoder approaches.
The paper tackles the problem of detecting novel data in AI-based autonomous systems by proposing a new autoencoder architecture with semantic guidelines and error calculation, which reduces false negatives compared to existing methods.
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.