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

Authors: Zhuang Liu, Kaiming He
Published: 2024-03-13 | Updated: 2025-03-03

Source: https://arxiv.org/abs/2403.08632

PDF: https://arxiv.org/pdf/2403.08632

AIにより推定されたラベル

学習データのバイアス排除 深層学習 データキュレーション

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

Abstract

We revisit the “dataset classification” experiment suggested by Torralba & Efros (2011) 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 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 explained by memorization. We hope our discovery will inspire the community to rethink issues involving dataset bias.