  Extrapolation -- the ability to make inferences that go beyond the scope of
one's experiences -- is a hallmark of human intelligence. By contrast, the
generalization exhibited by contemporary neural network algorithms is largely
limited to interpolation between data points in their training corpora. In this
paper, we consider the challenge of learning representations that support
extrapolation. We introduce a novel visual analogy benchmark that allows the
graded evaluation of extrapolation as a function of distance from the convex
domain defined by the training data. We also introduce a simple technique,
temporal context normalization, that encourages representations that emphasize
the relations between objects. We find that this technique enables a
significant improvement in the ability to extrapolate, considerably
outperforming a number of competitive techniques.
