Distribution Detection

Out-of-distribution (OOD) detection aims to identify data points that differ significantly from a machine learning model's training data, crucial for ensuring reliable and safe model deployment. Current research focuses on developing novel scoring functions and model architectures, including those based on diffusion models, variational autoencoders, and vision-language models, to improve the accuracy and efficiency of OOD detection, often addressing challenges posed by imbalanced datasets and limited access to model parameters. This field is vital for enhancing the trustworthiness of AI systems across diverse applications, from autonomous driving to medical diagnosis, by mitigating the risks associated with making predictions on unseen data. A growing emphasis is placed on developing methods that are both effective and computationally efficient, particularly for resource-constrained environments.