Visual Analogy Extrapolation Challenge (VAEC)

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, 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.