用局部学习规则训练非平衡物理生成模型，实现高效采样与图像生成。
Local Learning Rules for Out-of-Equilibrium Physical Generative Models
- 通过力测量或系统动态直接计算驱动协议梯度
- 在2D混合高斯和MNIST数据上成功生成图像
- 适用于物理可实现的生成模型，适合硬件部署
我们证明了基于得分的生成模型（SGMs）的非平衡驱动协议可通过局部学习规则进行学习。参数梯度可直接从力测量或观测到的系统动力学中计算得出。作为演示，我们在一个受驱、非线性、阻尼振子网络与热浴耦合的系统中实现了SGM。首先将其应用于二维混合高斯分布的采样问题。最后，将振子网络在MNIST数据集上训练，用于生成数字0和1的手写图像。
原文摘要 · Abstract (English)
We show that the out-of-equilibrium driving protocol of score-based generative models (SGMs) can be learned via local learning rules. The gradient with respect to the parameters of the driving protocol is computed directly from force measurements or from observed system dynamics. As a demonstration, we implement an SGM in a network of driven, nonlinear, overdamped oscillators coupled to a thermal bath. We first apply it to the problem of sampling from a mixture of two Gaussians in 2D. Finally, we train an oscillator network on the MNIST dataset to generate images of handwritten digits 0 and 1.
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