We propose a novel two-stage framework of generative models named Debiasing Kernel-Based Generative Models (DKGM) with the insights from kernel density estimation (KDE) and stochastic approximation. In the first stage of DKGM, we employ KDE to bypass the obstacles in estimating the density of data without losing too much image quality. One characteristic of KDE is oversmoothing, which makes the generated image blurry. Therefore, in the second stage, we formulate the process of reducing the blurriness of images as a statistical debiasing problem and develop a novel iterative algorithm to improve image quality, which is inspired by the stochastic approximation. Extensive experiments illustrate that the image quality of DKGM on CIFAR10 is comparable to state-of-the-art models such as diffusion models and GAN models. The performance of DKGM on CelebA 128x128 and LSUN (Church) 128x128 is also competitive. We conduct extra experiments to exploit how the bandwidth in KDE affects the sample diversity and debiasing effect of DKGM. The connections between DKGM and score-based models are also discussed.

翻译：我们提出了一种新颖的两阶段生成模型框架，称为基于核的生成模型去偏方法（DKGM），其设计灵感来源于核密度估计（KDE）与随机逼近理论。在DKGM的第一阶段，我们采用KDE来绕过直接估计数据密度的困难，同时尽可能保持图像质量。KDE的一个特点是过度平滑，这会导致生成图像模糊。因此，在第二阶段，我们将降低图像模糊度的过程形式化为一个统计去偏问题，并受随机逼近思想启发，开发了一种新颖的迭代算法以提升图像质量。大量实验表明，DKGM在CIFAR10数据集上的图像质量可与扩散模型和GAN模型等先进模型相媲美。DKGM在CelebA 128x128和LSUN（Church） 128x128数据集上的表现也具备竞争力。我们通过额外实验探究了KDE中带宽参数如何影响DKGM的样本多样性与去偏效果，并讨论了DKGM与基于分数的生成模型之间的联系。