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      Training Unbiased Diffusion Models From Biased Dataset

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          Abstract

          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.

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          Author and article information

          Journal
          02 March 2024
          Article
          2403.01189
          5f64103e-b143-42f8-ab57-7ed209187649

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          International Conference on Learning Representations (ICLR 2024)
          cs.LG cs.CV

          Computer vision & Pattern recognition,Artificial intelligence
          Computer vision & Pattern recognition, Artificial intelligence

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