A Decade's Battle on Dataset Bias: Are We There Yet?

arXiv:2403.08632v1 Announce Type: cross
Abstract: 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. …

abstract accuracy architectures arxiv bias classification cs.cv cs.lg dataset datasets diverse experiment modern network networks neural network neural networks observe scale type