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Structured Prior Knowledge (SPK) is a framework that explicitly extracts latent semantic, geometric, and contextual priors from pretrained object detectors to achieve state-of-the-art out-of-distribution detection, improving interpretability and reliability.
This paper introduces a novel method using sparse autoencoders (SAEs) to learn interpretable features from intermediate network activations for out-of-distribution (OOD) detection, achieving state-of-the-art performance and providing insights into how distribution shifts affect learned representations.
This paper presents a Mahalanobis-guided latent out-of-distribution detection method using a VAE to switch between a reinforcement learning controller and an extremum seeking controller in time-varying systems, validated in particle accelerator control.
A mathematically principled framework, Variational Inference Evidential Deep Learning (VI-EDL), is proposed to address limitations in conventional Evidential Deep Learning by reformulating it through variational inference, deriving an Evidence Lower Bound, establishing a generalization bound, and achieving state-of-the-art performance on visual and medical datasets.
This paper introduces GOEN, a pipeline combining multi-scale features, L2 normalization, and Mahalanobis distance for OOD detection, and finds that CenterLoss regularization actually degrades OOD performance despite improving classification accuracy.
This paper introduces DOSER, a framework using diffusion models for out-of-distribution detection and selective regularization in offline reinforcement learning. It aims to improve performance on static datasets by distinguishing between beneficial and detrimental OOD actions.