Novelty in Image Classification

In this chapter, we introduce real-world data in the form of RGB images and expand on the application of the underlying theory. We divide the world of images into known and novel sets and then use a sampling process to generate a large number of novelty experiments. There is a learning-based novelty-aware classification agent, but it does not actively interact with the world. For the image classification task, the perceptual operators are defined as the features computed from a Deep Neural Network (DNN) trained on the classification task using the known classes.