Geometry over Density: Few-Shot Cross-Domain OOD Detection

arXiv:2605.03410v2 Announce Type: replace
Abstract: Out-of-distribution (OOD) detection identifies test samples that fall outside a model’s training distribution, a capability critical for safe deployment in high-stakes applications. Standard OOD detectors are trained on a specific in-distribution (ID) dataset and detect deviations from that single domain. In contrast, we study few-shot cross-domain OOD detection: given a emphsingle pre-trained model, can we perform OOD detection on empharbitrary new ID-OOD task pairs using only a handful of ID samples at inference time, with no additional training? We propose textbfUFCOD, a unified framework that achieves this goal through information-geometric analysis of diffusion trajectories. Our key insight is that diffusion noise predictions are score functions (gradients of log-density), and we extract two energy features: emphPath Energy (integrated score magnitude) and emphDynamics Energy (score smoothness), that form a discrete Sobolev norm capturing how samples interact with the learned diffusion process. The central contribution is a textbftrain-once, deploy-anywhere paradigm: a diffusion model trained on a single dataset (e.g., CelebA) serves as a universal feature extractor for OOD detection across semantically unrelated domains (e.g., CIFAR-10, SVHN, Textures). At deployment, each new task requires only $sim$100 unlabeled ID samples for inference: no retraining, no fine-tuning, no task-specific adaptation. Using 100 ID samples per task, UFCOD achieves 93.7% average AUROC across 12 cross-domain benchmarks, competitive with methods trained on 50k–163k samples, demonstrating $sim$500$times$ improvement in sample efficiency. See our code in https://github.com/lili0415/UFCOD.