Distribution Datasets

Distribution datasets research focuses on improving the ability of machine learning models to reliably identify and handle data points that differ significantly from their training data (out-of-distribution or OOD detection). Current research emphasizes developing novel algorithms and model architectures, such as those based on logit scaling, Gaussian mixture models, diffusion models, and energy-based methods, to enhance OOD detection accuracy and robustness across various model types and datasets. This work is crucial for ensuring the safe and reliable deployment of AI systems in real-world scenarios where encountering unexpected data is inevitable, impacting fields ranging from autonomous driving to medical diagnosis.