Do CIFAR-10 Classifiers Generalize to CIFAR-10?

TL;DR

This paper evaluates whether CIFAR-10 classifiers truly generalize by testing them on a newly collected, unseen dataset, revealing significant accuracy drops and questioning the robustness of current models.

Contribution

The study introduces a new unseen test set for CIFAR-10 and demonstrates that existing models' accuracy significantly declines, highlighting issues with overfitting and data distribution sensitivity.

Findings

Large accuracy drop (4-10%) on new test set

More recent models show smaller accuracy decline

Current accuracy metrics are brittle and data-sensitive

Abstract

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.