Distribution Detection Method

Out-of-distribution (OOD) detection aims to identify data points that differ significantly from the training distribution of a machine learning model, crucial for reliable and safe deployment. Recent research focuses on developing efficient and robust OOD detection methods, exploring approaches like low-rank approximation of model weights, probabilistic contrastive learning within spherical embedding spaces, and likelihood-based methods using diffusion models. These advancements improve accuracy and efficiency, addressing challenges such as adversarial examples and the need for compatibility with in-distribution performance, ultimately enhancing the reliability and trustworthiness of machine learning systems across various applications.