arXiv:2606.07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty modeling for few-shot classification and out-of-distribution (OOD) detection. While CLIP achieves strong zero-shot recognition, it yields deterministic similarity scores and offers limited uncertainty information, which is critical under distribution shift and data scarcity. GP-Adapter constructs modality-specific, class-wise one-class GPs on top of frozen CLIP embeddings using an RBF kernel for im
The proliferation of complex AI models like CLIP necessitates robust uncertainty estimation to improve reliability and safety, especially in few-shot and OOD scenarios critical for real-world deployment.
This development enhances the practical utility of foundational models by addressing their limitations in uncertainty quantification, moving them closer to reliable autonomous decision-making.
AI systems built on large pre-trained models can now incorporate more sophisticated uncertainty awareness, improving their performance and trustworthiness in novel or outlier situations without extensive retraining.
- · AI developers and researchers
- · Industries deploying AI in critical applications (e.g., healthcare, autonomous v
- · Users benefiting from more reliable AI systems
- · Legacy AI uncertainty methodologies
- · Systems heavily reliant on retraining for OOD detection
GP-Adapter provides a training-free method to add uncertainty quantification to large pre-trained models like CLIP, improving their robustness and OOD detection capabilities.
This advancement could accelerate the deployment of AI agents and autonomous systems by increasing their reliability and safety in diverse and unpredictable environments.
Improved uncertainty modeling might reduce the need for extensive human oversight in AI systems, potentially reshaping workflows and the adoption rate of AI in sensitive applications.
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Read at arXiv cs.AI