  We revisit the "dataset classification" experiment suggested by Torralba and
Efros a decade ago, in the new era with large-scale, diverse, and hopefully
less biased datasets as well as more capable neural network architectures.
Surprisingly, we observe that modern neural networks can achieve excellent
accuracy in classifying which dataset an image is from: e.g., we report 84.7%
accuracy on held-out validation data for the three-way classification problem
consisting of the YFCC, CC, and DataComp datasets. Our further experiments show
that such a dataset classifier could learn semantic features that are
generalizable and transferable, which cannot be simply explained by
memorization. We hope our discovery will inspire the community to rethink the
issue involving dataset bias and model capabilities.
