Conditional diffusion models have shown remarkable performance in various
generative tasks, but training them requires large-scale datasets that often
contain noise in conditional inputs, a.k.a. noisy labels. This noise leads to
condition mismatch and quality degradation of generated data. This paper
proposes Transition-aware weighted Denoising Score Matching (TDSM) for training
conditional diffusion models with noisy labels, which is the first study in the
line of diffusion models. The TDSM objective contains a weighted sum of score
networks, incorporating instance-wise and time-dependent label transition
probabilities. We introduce a transition-aware weight estimator, which
leverages a time-dependent noisy-label classifier distinctively customized to
the diffusion process. Through experiments across various datasets and noisy
label settings, TDSM improves the quality of generated samples aligned with
given conditions. Furthermore, our method improves generation performance even
on prevalent benchmark datasets, which implies the potential noisy labels and
their risk of generative model learning. Finally, we show the improved
performance of TDSM on top of conventional noisy label corrections, which
empirically proving its contribution as a part of label-noise robust generative
models. Our code is available at: this https URL

使用 Transition-aware weighted Denoising Score Matching（TDSM）训练带有噪声标签的条件扩散模型，其中 TDSM 目标函数包含得分网络的加权和，并整合了实例级和时间相关的标签转移概率。通过在各种数据集和噪声标签设置上进行实验证明，TDSM 可以提高与给定条件相匹配的生成样本的质量，甚至在常见基准数据集上也能提高生成性能，从而证明了噪声标签对于生成模型学习的潜在风险以及 TDSM 在传统噪声标签修正的基础上进一步提高性能的实证贡献。

标签噪声鲁棒扩散模型

Label-Noise Robust Diffusion Models

We argue that the theory and practice of diffusion-based generative models
are currently unnecessarily convoluted and seek to remedy the situation by
presenting a design space that clearly separates the concrete design choices.
This lets us identify several changes to both the sampling and training
processes, as well as preconditioning of the score networks. Together, our
improvements yield new state-of-the-art FID of 1.79 for CIFAR-10 in a
class-conditional setting and 1.97 in an unconditional setting, with much
faster sampling (35 network evaluations per image) than prior designs. To
further demonstrate their modular nature, we show that our design changes
dramatically improve both the efficiency and quality obtainable with
pre-trained score networks from previous work, including improving the FID of a
previously trained ImageNet-64 model from 2.07 to near-SOTA 1.55, and after
re-training with our proposed improvements to a new SOTA of 1.36.

我们提出了一种设计空间，清晰地分离具体的设计选择，从采样和训练过程以及得分网络的预处理中识别了几个变化。通过改进，我们以比之前的设计更快的采样速度（每张图像 35 个网络评估）在条件设置下获得了 1.79 FID，无条件设置下获得了 1.97 FID，达到了新的最先进水平。