Insights gained from the manifold-based novelty detection can indeed be leveraged to develop more interpretable and explainable models for anomaly identification. By understanding the latent space representation of the data distribution, it becomes possible to interpret how the model distinguishes between normal and anomalous instances.
One approach to enhancing interpretability is to visualize the latent space and the learned manifold to identify regions where anomalies are more likely to occur. By analyzing the distribution of inliers and outliers in the latent space, it becomes easier to explain why certain data points are classified as anomalies. Additionally, techniques like saliency mapping or attention mechanisms can be employed to highlight the features or dimensions in the latent space that contribute most to the detection of anomalies.
Furthermore, incorporating domain knowledge or domain-specific constraints into the model can help in creating more interpretable anomaly detection systems. By aligning the model's decision-making process with domain-specific rules or expectations, the model's outputs can be more easily understood and explained to end-users or stakeholders.