Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection
This addresses the need for reliable and interpretable AI in safety-relevant domains, representing an incremental advancement in combining explainability with OOD detection.
The paper tackles the problem of making deep learning models more trustworthy for safety-critical applications by developing an explainable out-of-distribution detection method, achieving improved performance over previous methods on benchmarks and a real-world railway dataset.
Understanding the decision-making and trusting the reliability of Deep Machine Learning Models is crucial for adopting such methods to safety-relevant applications. We extend self-explainable Prototypical Variational models with autoencoder-based out-of-distribution (OOD) detection: A Variational Autoencoder is applied to learn a meaningful latent space which can be used for distance-based classification, likelihood estimation for OOD detection, and reconstruction. The In-Distribution (ID) region is defined by a Gaussian mixture distribution with learned prototypes representing the center of each mode. Furthermore, a novel restriction loss is introduced that promotes a compact ID region in the latent space without collapsing it into single points. The reconstructive capabilities of the Autoencoder ensure the explainability of the prototypes and the ID region of the classifier, further aiding the discrimination of OOD samples. Extensive evaluations on common OOD detection benchmarks as well as a large-scale dataset from a real-world railway application demonstrate the usefulness of the approach, outperforming previous methods.