AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
Understanding Out of Distribution Detection in Machine Learning
This chapter explores the critical concept of out of distribution (OOD) detection in machine learning, highlighting the challenges models face when exposed to new data that differs from their training sets. It emphasizes the importance of robust models in ensuring AI systems' performance and safety, particularly in high-stakes applications like self-driving cars and healthcare. The discussion includes methodologies for detecting OOD scenarios, the complexity of open set recognition, and the implications for model explainability.