We introduce a novel sampler called the energy based diffusion generator for generating samples from arbitrary target distributions. The sampling model employs a structure similar to a variational autoencoder, utilizing a decoder to transform latent variables from a simple distribution into random variables approximating the target distribution, and we design an encoder based on the diffusion model. Leveraging the powerful modeling capacity of the diffusion model for complex distributions, we can obtain an accurate variational estimate of the Kullback-Leibler divergence between the distributions of the generated samples and the target. Moreover, we propose a decoder based on generalized Hamiltonian dynamics to further enhance sampling performance. Through empirical evaluation, we demonstrate the effectiveness of our method across various complex distribution functions, showcasing its superiority compared to existing methods.

翻译：我们提出一种名为基于能量的扩散生成器的新型采样器，用于从任意目标分布中生成样本。该采样模型采用类似变分自编码器的结构，利用解码器将来自简单分布的潜变量转换为近似目标分布的随机变量，并基于扩散模型设计了编码器。凭借扩散模型对复杂分布的强大建模能力，我们能够获得生成样本分布与目标分布之间KL散度的精确变分估计。此外，我们提出基于广义哈密顿动力学的解码器以进一步提升采样性能。通过实证评估，我们证明了该方法在多种复杂分布函数上的有效性，并展示了其相较于现有方法的优越性。