Model Similarity Mitigates Test Set Overuse
This addresses the issue of test set overuse for researchers and practitioners in ML, offering a theoretical explanation for benchmark longevity, but it is incremental as it builds on existing concerns about data reuse.
The paper tackles the problem of excessive test data reuse in machine learning by showing that model similarity mitigates overfitting, proving through empirical evidence on ImageNet and hyperparameter search that models agree in predictions beyond accuracy levels, and providing a non-asymptotic generalization bound for practical confidence.
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.