Title: EviMix: Evidential Deep Learning with Latent-Space Mixing for Uncertainty Quantification and OOD Detection
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: uncertainty quantification, evidential deep learning, feature space mixing, reliability, robustness, supervised learning, representation learning, predictive uncertainty, out-of-distribution detection, aleatoric uncertainty, epistemic uncertainty, data augmentation, stress testing, calibration, trustworthiness, autonomous systems

Abstract: Reliable uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical domains such as autonomous driving and medical imaging. Evidential Deep Learning (EDL) provides a computationally efficient framework for estimating epistemic and aleatoric uncertainty through Dirichlet evidence assignment, enabling real-time uncertainty estimation. However, recent studies have raised concerns about its robustness, including conflation of uncertainty types, persistent epistemic uncertainty under abundant data, and sensitivity to training dynamics. Moreover, the interaction between EDL and modern data augmentation strategies remains poorly understood.
In this work, we make three key contributions: (1) we systematically study the impact of popular pixel-space augmentations on EDL’s uncertainty estimates; (2) we introduce LatentMix, a feature-space augmentation framework that interpolates latent representations across multiple network depths using both in-batch cross-class mixing and an external mixing set; mixing severities are sampled per layer, with early layers emphasizing stronger noise perturbations and later layers capturing finer-grained semantic variations; and (3) we propose a mechanism whereby these sampled severities directly regulate the EDL loss, explicitly shaping both aleatoric and epistemic uncertainty.
Our experiments demonstrate that the proposed LatentMix significantly improves out-of-distribution detection, sharpens the separation between aleatoric and epistemic uncertainty, and enhances calibration, outperforming pixel-space and single-layer mixing methods.

TL;DR: We introduce LatentMix, a feature-space augmentation strategy that disentangles aleatoric and epistemic uncertainty in Evidential Deep Learning, improving calibration, robustness, and OOD detection beyond pixel-space methods.