Distribution Sample

Distribution sample 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 samples). Current research emphasizes developing novel methods for OOD detection, often leveraging techniques like generative models, feature normalization, and knowledge distillation within various architectures including CNNs and transformers. This work is crucial for enhancing the robustness and safety of AI systems across diverse applications, particularly in safety-critical domains like medical image analysis and autonomous driving, where misclassifying OOD samples can have serious consequences.