Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection

TL;DR

This paper introduces an explainable out-of-distribution detection method combining prototypical variational autoencoders with a novel restriction loss, improving reliability and interpretability in safety-critical applications.

Contribution

It extends self-explainable prototypical variational models with autoencoder-based OOD detection and a new restriction loss for a compact in-distribution region.

Findings

Outperforms previous OOD detection methods on benchmark datasets

Provides explainability through reconstructive autoencoder capabilities

Demonstrates effectiveness on large-scale real-world railway data

Abstract

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.