Classification Logit Two-sample Testing by Neural Networks
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The recent success of generative adversarial networks and variational learning suggests training a classifier network may work well in addressing the classical two-sample problem. Network-based tests have the computational advantage that the algorithm scales to large samples. This paper proposes to use the difference of the logit of a trained neural network classifier evaluated on the two finite samples as the test statistic. Theoretically, we prove the testing power to differentiate two smooth densities given that the network is sufficiently parametrized, by comparing the learned logit function to the log ratio of the densities, the latter maximizing the population training objective. When the two densities lie on or near to low-dimensional manifolds embedded in possibly high-dimensional space, the needed network complexity is reduced to only depending on the intrinsic manifold geometry. In experiments, the method demonstrates better performance than previous network-based tests which use the classification accuracy as the test statistic, and compares favorably to certain kernel maximum mean discrepancy (MMD) tests on synthetic and hand-written digits datasets.
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