论文标题
使用扩散模型从低密度区域生成高富达数据
Generating High Fidelity Data from Low-density Regions using Diffusion Models
论文作者
论文摘要
我们的工作着重于解决常见图像数据集中数据歧管低密度区域的样本缺陷。我们利用基于扩散过程的生成模型来合成来自低密度区域的新图像。我们观察到来自扩散模型的均匀采样主要是来自数据歧管高密度区域的样品。因此，我们修改采样过程以将其引导到低密度区域，同时保持合成数据的保真度。我们严格地证明我们的过程成功地生成了来自低密度区域的新型高保真样本。我们进一步检查了生成的样品，并表明该模型不会记住低密度数据，并且确实学会了从低密度区域生成新样本。
Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold. Therefore, we modify the sampling process to guide it towards low-density regions while simultaneously maintaining the fidelity of synthetic data. We rigorously demonstrate that our process successfully generates novel high fidelity samples from low-density regions. We further examine generated samples and show that the model does not memorize low-density data and indeed learns to generate novel samples from low-density regions.
