Distributional Uncertainty for Out-of-Distribution Detection
This addresses the challenge of detecting out-of-distribution samples in computer vision tasks, such as segmentation, by providing a more semantically aligned and efficient uncertainty estimation method, though it appears incremental as it builds on existing frameworks like RPL.
The paper tackled the problem of out-of-distribution detection in deep neural networks by proposing the Free-Energy Posterior Network, which jointly models distributional uncertainty and identifies OoD and misclassified regions using free energy, achieving improved performance on benchmarks like Fishyscapes, RoadAnomaly, and Segment-Me-If-You-Can.
Estimating uncertainty from deep neural networks is a widely used approach for detecting out-of-distribution (OoD) samples, which typically exhibit high predictive uncertainty. However, conventional methods such as Monte Carlo (MC) Dropout often focus solely on either model or data uncertainty, failing to align with the semantic objective of OoD detection. To address this, we propose the Free-Energy Posterior Network, a novel framework that jointly models distributional uncertainty and identifying OoD and misclassified regions using free energy. Our method introduces two key contributions: (1) a free-energy-based density estimator parameterized by a Beta distribution, which enables fine-grained uncertainty estimation near ambiguous or unseen regions; and (2) a loss integrated within a posterior network, allowing direct uncertainty estimation from learned parameters without requiring stochastic sampling. By integrating our approach with the residual prediction branch (RPL) framework, the proposed method goes beyond post-hoc energy thresholding and enables the network to learn OoD regions by leveraging the variance of the Beta distribution, resulting in a semantically meaningful and computationally efficient solution for uncertainty-aware segmentation. We validate the effectiveness of our method on challenging real-world benchmarks, including Fishyscapes, RoadAnomaly, and Segment-Me-If-You-Can.