# Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
## 9 Summary and Outlook  
ML is generally known to lack robustness, for example, in the case that the training data did rarely or completely not cover critical situations. Nevertheless, many AI-based systems fulfill safety-critical tasks such as in the case of AVs. Engineers cannot completely test and verify autonomous systems in advance during development due to many reasons. Thus, novelty detection for automatically identifying new relevant data differing from the available training data becomes an interesting approach to improve the robustness of AI-based systems, and thereby improve their safety. In this study, we successfully reproduced the results of a \"naive\" autoencoder-based novelty detection approach published in [9]. On the basis of a benchmark with a simple classification neural network, we demonstrated that such a \"naive\" approach is incapable of minimizing false negatives, when novel and known input data are similarly structured but semantically different. To reduce the number of false negatives, we proposed a semi-semantic approach, which used a semantic topology to ensure that the autoencoder reflected the semantics of the monitored system's output using several derived rules. These architectural rules were postulated and have been validated with test samples under our observation. To validate the rules, we also considered networks out of the boundaries of these rules. However, they could not beat the networks within the boundaries of the rules. Hence, in our experiments, the semi-semantic approach reduced the percentage of false negatives in unknown digits from 84% to 4%. However, the three rules have only been extensively evaluated, validated, and adjusted in the context of the MNIST dataset. Therefore, further ongoing research is required. Moreover, we discovered that the classification criterion for novelty detection, which is a threshold solely based on MSE, limits the semi-semantic approach's ability to reduce the number of false negatives. To overcome this limitation, we proposed a fully semantic approach, which relied on a semantic loss instead of on the original MSE. The semantic loss is realized by a new architectural structure. Instead of comparing with the input data, the loss is calculated based on the output of the AI-based system, in our case referring to the digit classification function, fed with the original input data as well as with the reconstructed input data. Thus, the fully semantic approach can detect novelty on the input data level and also on a higher semantic level. Consequently, the critical cases of novelty detection regarding similarly structured but semantically different novel data were eliminated. According to the comparison of the fully semantic and the semi-semantic approaches in our experiments, the percentage of false negative errors for the classification of unknown digits was reduced from 4% to 0%. The results of our study demonstrate that semantic novelty detection can prevent false negative errors, especially for structurally similar but semantically different input data. However, false positive errors increase. From a safety perspective, false positive errors are less critical than false negative errors in novelty detection. In this study, datasets such as handwritten digit images and letters are used in the experiments, which are different from data used in the real-world domain for AVs. Another limitation is that our test samples and experiments do not allow generalizing the presented results (architectural rules and structure). In the future, real traffic images are required to  
Figure 12: Performance of applying the fully semantic novelty detection. be used to evaluate the proposed semantic novelty detection. The postulated architectural rules must be validated with more experiments. Moreover, other network types should be evaluated.