TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space
TopoMap is a model-agnostic method that creates a topographical map of input feature space to find regions where Deep Learning models are likely to fail. It works by extracting input features, reducing their dimensionality, and clustering them. A Deep Neural Network is then used to automatically select the best combination of feature reduction and clustering techniques for creating meaningful failure regions.