Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions by differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which involve training a pushforward generator network. In this paper, we challenge this interpretation and propose a novel framework that unifies particle and adversarial generative models by framing generator training as a generalization of particle models. This suggests that a generator is an optional addition to any such generative model. Consequently, integrating a generator into a score-based diffusion model and training a GAN without a generator naturally emerge from our framework. We empirically test the viability of these original models as proofs of concepts of potential applications of our framework.

翻译：基于粒子的深度生成模型，例如梯度流和基于得分的扩散模型，因其卓越性能近年来备受关注。这些模型通过微分方程驱动粒子分布演化的原理，通常被认为与先前广泛使用的生成对抗网络（GANs）截然不同，后者需要对一个前推生成网络进行训练。本文对这一解读提出质疑，并构建了一个新颖框架，通过将生成器训练视为粒子模型的泛化形式，从而统一了粒子生成模型与对抗生成模型。该框架表明，生成器是此类生成模型的可选附加组件。由此，我们的框架自然衍生出将生成器集成到基于得分的扩散模型，以及无需生成器即可训练GAN的两种新范式。我们通过实证测试验证了这些原始模型的可行性，作为该框架潜在应用的概念验证。