用评分判别器修正噪声标签，提升扩散模型生成质量
Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction
- 基于对抗损失训练判别器，评估样本真实性以引导生成
- 仅在生成初期使用修正，性能优于现有方法
- 无需重训练，计算开销小，适合实际部署
扩散模型在图像与视频生成中表现卓越，尤其擅长利用大规模数据集。然而，这些数据集常因人工标注产生错误。本文提出基于评分的判别器修正（SBDC），一种用于对齐带噪声预训练条件扩散模型的引导技术。该方法通过对抗损失训练判别器，借鉴已有噪声检测技术评估样本真实性。实验表明，仅在生成早期阶段应用该引导可获得更优性能。所提方法计算高效，推理时间几乎无增加，且无需重新训练扩散模型。在多种噪声设置下的实验验证了其优于当前最优方法。
原文摘要 · Abstract (English)
Diffusion models have gained prominence as state-of-the-art techniques for synthesizing images and videos, particularly due to their ability to scale effectively with large datasets. Recent studies have uncovered that these extensive datasets often contain mistakes from manual labeling processes. However, the extent to which such errors compromise the generative capabilities and controllability of diffusion models is not well studied. This paper introduces Score-based Discriminator Correction (SBDC), a guidance technique for aligning noisy pre-trained conditional diffusion models. The guidance is built on discriminator training using adversarial loss, drawing on prior noise detection techniques to assess the authenticity of each sample. We further show that limiting the usage of our guidance to the early phase of the generation process leads to better performance. Our method is computationally efficient, only marginally increases inference time, and does not require retraining diffusion models. Experiments on different noise settings demonstrate the superiority of our method over previous state-of-the-art methods.
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