Title: Out-of-Distribution Classification and Clustering. Abstract: One of the long-term goals of machine learning is to develop models which will generalize well enough that they can handle a broader range of circumstances than what they were trained on. In the context of vision, such a model would not get confounded by elements outside of its training distribution. To this end, we propose a new task for training neural networks, in which the goal is to determine if all images in a given set share the same class. We demonstrate that a model trained with this task can classify and cluster samples from out-of-distribution classes. This includes left out classes from the same dataset, as well as entire datasets never trained on. Our experiments also reveal an unreported phenomenon, which is that neural networks can overfit their training classes, leading to poorer out-of-distribution performance. It is our belief that mitigating this effect and improving on our task will lead to better out-of-distribution generalization as well as behaviours more resembling those of humans.