  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 https://github.com/alsdudrla10/TIW-DSM.
