Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model

arXiv:2403.13324v1 Announce Type: new
Abstract: Out-of-distribution (OOD) detection is a critical task to ensure the reliability and security of machine learning models deployed in real-world applications. Conventional methods for OOD detection that rely on single-modal information, often struggle to capture the rich variety of OOD instances. The primary difficulty in OOD detection arises when an input image has numerous similarities to a particular class in the in-distribution (ID) dataset, e.g., wolf to dog, causing the model to misclassify it. Nevertheless, …

abstract applications arxiv class cs.cv detection distribution generated information instances language language model large language large language model machine machine learning machine learning models modal peer reliability security struggle type world