With significant advancements in diffusion models, addressing the potential
risks of dataset bias becomes increasingly important. Since generated outputs
directly suffer from dataset bias, mitigating latent bias becomes a key factor
in improving sample quality and proportion. This paper proposes time-dependent
importance reweighting to mitigate the bias for the diffusion models. We
demonstrate that the time-dependent density ratio becomes more precise than
previous approaches, thereby minimizing error propagation in generative
learning. While directly applying it to score-matching is intractable, we
discover that using the time-dependent density ratio both for reweighting and
score correction can lead to a tractable form of the objective function to
regenerate the unbiased data density. Furthermore, we theoretically establish a
connection with traditional score-matching, and we demonstrate its convergence
to an unbiased distribution. The experimental evidence supports the usefulness
of the proposed method, which outperforms baselines including time-independent
importance reweighting on CIFAR-10, CIFAR-100, FFHQ, and CelebA with various
bias settings. Our code is available at this https URL

本文提出了一种时间相关的重要性重新加权方法来减轻扩散模型中的数据集偏差，并证明了时间相关密度比方法相较于之前的方法更准确，从而在生成学习中最小化误差传播。通过将时间相关密度比用于重新加权和得分校正，可以获得一个可行的目标函数来重建无偏数据密度。此外，本文理论上建立了与传统得分匹配的联系，并证明了其收敛于无偏分布。实验证据支持了该方法的有效性，它在包括 CIFAR-10、CIFAR-100、FFHQ 和 CelebA 在内的多种偏差设置下胜过了基线方法。