We all know that reliability is the Achilles’ heel of modern ML, as predictions are often wrong for out-of-distribution (OOD) inputs that do not resemble previo

We all know that reliability is the Achilles’ heel of modern ML, as predictions are often wrong for out-of-distribution (OOD) inputs that do not resemble previous training data. While scientists have proposed many complex OOD detection algorithms for specific data types, our latest research demonstrates straightforward methods that work just as effectively and work directly with any type of data for which either a feature embedding or trained classifier is available. We’ve open-sourced our methods with easy tutorials and published lots of content describing them and benchmarking their performance for detecting OOD images. Hope this helps make your ML more trustworthy!