Distribution Detector

Out-of-distribution (OOD) detection aims to identify data points that deviate from a machine learning model's training distribution, crucial for ensuring the safety and reliability of deployed systems. Current research focuses on improving the efficiency and robustness of OOD detectors, exploring various architectures like variational autoencoders, vision transformers, and large language models, as well as combining existing methods and leveraging techniques like ensemble learning and loss landscape analysis. This field is vital for enhancing the trustworthiness of AI systems across diverse applications, from autonomous vehicles and medical diagnosis to cybersecurity and chemical sensing, by mitigating the risks associated with unexpected inputs.