Do CIFAR-10 Classifiers Generalize to CIFAR-10?
This work addresses the reliability of accuracy claims in ML for researchers and practitioners, highlighting a critical issue in model evaluation, though it is incremental as it builds on existing concerns about test set reuse.
The authors tackled the problem of overfitting in machine learning by creating a new test set for CIFAR-10 to measure classifier generalization, finding a 4% to 10% drop in accuracy for deep learning models, indicating that current accuracy metrics are brittle to natural data variations.
Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by creating a new test set of truly unseen images. Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models. Yet more recent models with higher original accuracy show a smaller drop and better overall performance, indicating that this drop is likely not due to overfitting based on adaptivity. Instead, we view our results as evidence that current accuracy numbers are brittle and susceptible to even minute natural variations in the data distribution.