Synthetic samples from diffusion models are promising for leveraging in training discriminative models as replications of real training datasets. However, we found that the synthetic datasets degrade classification performance over real datasets even when using state-of-the-art diffusion models. This means that modern diffusion models do not perfectly represent the data distribution for the purpose of replicating datasets for training discriminative tasks. This paper investigates the gap between synthetic and real samples by analyzing the synthetic samples reconstructed from real samples through the diffusion and reverse process. By varying the time steps starting the reverse process in the reconstruction, we can control the trade-off between the information in the original real data and the information added by diffusion models. Through assessing the reconstructed samples and trained models, we found that the synthetic data are concentrated in modes of the training data distribution as the reverse step increases, and thus, they are difficult to cover the outer edges of the distribution. Our findings imply that modern diffusion models are insufficient to replicate training data distribution perfectly, and there is room for the improvement of generative modeling in the replication of training datasets.

翻译：扩散模型生成的合成样本有望作为真实训练数据集的复制品，用于训练判别模型。然而我们发现，即便使用最先进的扩散模型，合成数据集相较于真实数据集仍会导致分类性能下降。这意味着现代扩散模型无法完美表征用于复制训练数据集以完成判别任务的数据分布。本文通过分析经扩散与逆过程重构的真实样本，探究合成样本与真实样本之间的差异。通过调整重构过程中逆过程的起始时间步长，我们可以控制原始真实数据信息与扩散模型新增信息之间的权衡。通过对重构样本及训练模型的评估发现，随着逆过程步长增加，合成数据会集中于训练数据分布的众数区域，难以覆盖分布边缘。我们的研究结果表明，现代扩散模型尚不足以完美复刻训练数据分布，在生成式建模方法复制训练数据集的任务中存在改进空间。