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This paper introduces the Representation Gap, a metric for neural network generalization error with better asymptotic dynamics. Using a geometric perspective and optimal quantization theory, the authors show it is governed by the intrinsic dimension of the task, and verify this empirically on synthetic and realistic datasets.