When developing deep learning models, we usually decide what task we want to solve then search in the space of models in order to design one that generalizes well on this task. An intriguing question would be: what if, instead of fixing the task and searching in the model space, we fix the model and search in the task space? Can we find tasks that the model generalizes on? What do they look like, or do they show anything?This is the question we address in this paper. We propose a task discovery…