  One powerful paradigm in visual navigation is to predict actions from
observations directly. Training such an end-to-end system allows
representations useful for downstream tasks to emerge automatically. However,
the lack of inductive bias makes this system data inefficient. We hypothesize a
sufficient representation of the current view and the goal view for a
navigation policy can be learned by predicting the location and size of a crop
of the current view that corresponds to the goal. We further show that training
such random crop prediction in a self-supervised fashion purely on synthetic
noise images transfers well to natural home images. The learned representation
can then be bootstrapped to learn a navigation policy efficiently with little
interaction data. The code is available at https://yanweiw.github.io/noise2ptz
