Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models
arXiv:2605.23797v1 Announce Type: new Abstract: Aiming at identifying unexpected inputs from unknown classes, out-of-distribution (OOD) detection has emerged as a pivotal approach to enhancing the reliability of machine learning models. This paper focuses on the burgeoning paradigm of post-hoc OOD detection with pre-trained vision-language models (VLMs), where a popular pipeline is to detect OOD inputs by examining their affinities between ID labels and negative labels, i.e., those semantically different from ID labels. Due to the unavailability of target OOD labels, existing works predominant
The proliferation of complex AI models, particularly pre-trained vision-language models, necessitates enhanced reliability and safety mechanisms for deployment in critical applications, driving the urgency for robust OOD detection. This paper addresses a key methodological challenge in improving the detection of novel or unexpected inputs.
Improved out-of-distribution detection is crucial for the safe and reliable deployment of AI systems, especially as they become more autonomous and integrated into real-world applications, thus reducing the risk of failures from unforeseen inputs. It represents a foundational advancement in ensuring AI robustness and trustworthiness.
The proposed 'Debiased Negative Mining' technique offers a more effective method for identifying OOD inputs in pre-trained vision-language models, potentially leading to more resilient AI systems capable of handling unexpected scenarios. This refines existing pipelines for post-hoc OOD detection.
- · AI safety researchers
- · Developers of autonomous systems
- · Sectors deploying critical AI (e.g., healthcare, automotive)
- · Users benefiting from more reliable AI applications
- · AI systems prone to catastrophic failures from novel inputs
- · Legacy OOD detection methods
Increased trustworthiness and deployment of AI models in sensitive environments due to better handling of unknown inputs.
Reduced need for extensive manual oversight or re-training of AI systems when encountering new data distributions.
Acceleration of AI agent development by enabling them to operate more safely and autonomously without human intervention in unexpected situations.
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