Autoencoder-based semantic novelty detection : towards dependable AI-based systems

Rausch, Andreas GND; Sedeh, Azarmidokht Motamedi; Zhang, Meng

Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental 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, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, 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 the literature by minimizing false negatives.

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Rausch, Andreas / Sedeh, Azarmidokht Motamedi / Zhang, Meng: Autoencoder-based semantic novelty detection. towards dependable AI-based systems. 2021.

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