Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning

TL;DR

Scaling pretrained models enhances their geometric structure, enabling accurate label-free out-of-distribution detection without fine-tuning across vision and language tasks.

Contribution

This work demonstrates that larger pretrained representations inherently support effective label-free OOD detection, reducing reliance on specialized or fine-tuned models.

Findings

Performance of OOD detectors improves with representation quality.

Gaps between local and global detectors diminish as models scale.

Frozen pretrained models encode sufficient geometric information for OOD detection.

Abstract

Models trained with deep learning often fail to signal when inputs fall outside their training data manifold, leading to unreliable predictions under distribution shift. Prior work suggests that effective out-of-distribution (OOD) detection often requires class-conditional modeling or specialized models obtained through supervised fine-tuning. We revisit this assumption in modern pretrained models and show that their frozen representations already encode sufficient geometric structure for accurate label-free OOD detection. Across 59 backbone-task pairings spanning vision and language, we compare two complementary label-free detectors: a global Mahalanobis estimator fit on unlabeled latent representations, and ReSCOPED, a lightweight, diffusion-based typicality estimator operating on the same features at a local level. Despite their different detection mechanisms, representation scaling reveals a consistent regime-dependent pattern: both local and global detectors' absolute performance improves with better representation quality, and performance gaps between the two detectors disappear across both language and vision tasks as representations scale. These results suggest that label-free OOD detection depends strongly on the geometry exposed by frozen pretrained backbones, reducing the importance of detector choice as backbone scale increases and enabling efficient deployment directly on frozen models.