  The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked
datasets in computer vision and are often used to evaluate novel methods and
model architectures in the field of deep learning. However, we find that 3.3%
and 10% of the images from the test sets of these datasets have duplicates in
the training set. These duplicates are easily recognizable by memorization and
may, hence, bias the comparison of image recognition techniques regarding their
generalization capability. To eliminate this bias, we provide the "fair CIFAR"
(ciFAIR) dataset, where we replaced all duplicates in the test sets with new
images sampled from the same domain. We then re-evaluate the classification
performance of various popular state-of-the-art CNN architectures on these new
test sets to investigate whether recent research has overfitted to memorizing
data instead of learning abstract concepts. We find a significant drop in
classification accuracy of between 9% and 14% relative to the original
performance on the duplicate-free test set. The ciFAIR dataset and pre-trained
models are available at https://cvjena.github.io/cifair/, where we also
maintain a leaderboard.
