Out-of-Distribution Detection via Embeddings or Predictions

Researchers have developed two simpler methods for detecting Out-Of-Distribution (OOD) inputs, which are often neglected in complex OOD detection algorithms. These methods can be easily run on any type of data and do not require specific examples of outliers. The first method is based on feature embeddings, using the average distance to K-Nearest Neighbors (KNN) to score how atypical an example is. The second method uses predicted class probabilities output by a trained classifier to quantify uncertainty as a measure of outlyingness. Both methods are available as open-source code in cleanlab.outlier and can be used for various applications, such as autonomous vehicles where the system needs to detect unknown objects on the road.