Excessive reuse of test data has become commonplace in today's machine
learning workflows. Popular benchmarks, competitions, industrial scale tuning,
among other applications, all involve test data reuse beyond guidance by
statistical confidence bounds. Nonetheless, recent replication studies give
evidence that popular benchmarks continue to support progress despite years of
extensive reuse. We proffer a new explanation for the apparent longevity of
test data: Many proposed models are similar in their predictions and we prove
that this similarity mitigates overfitting. Specifically, we show empirically
that models proposed for the ImageNet ILSVRC benchmark agree in their
predictions well beyond what we can conclude from their accuracy levels alone.
Likewise, models created by large scale hyperparameter search enjoy high levels
of similarity. Motivated by these empirical observations, we give a
non-asymptotic generalization bound that takes similarity into account, leading
to meaningful confidence bounds in practical settings.

本文证明了在大尺度图像分类基准测试中，最新的提出的模型虽然其准确性相异，但其预测相似性很高，这一相似性缓解了过拟合问题。作者还根据这一相似性给出了一种新的泛化界限的数学模型，能够更好地应用于机器学习中测试数据的重复使用问题。