We present a novel algorithm for parameter learning in generic deep
generative models that builds upon the predictive coding (PC) framework of
computational neuroscience. Our approach modifies the standard PC algorithm to
bring performance on-par and exceeding that obtained from standard variational
auto-encoder (VAE) training. By injecting Gaussian noise into the PC inference
procedure we re-envision it as an overdamped Langevin sampling, which
facilitates optimisation with respect to a tight evidence lower bound (ELBO).
We improve the resultant encoder-free training method by incorporating an
encoder network to provide an amortised warm-start to our Langevin sampling and
test three different objectives for doing so. Finally, to increase robustness
to the sampling step size and reduce sensitivity to curvature, we validate a
lightweight and easily computable form of preconditioning, inspired by Riemann
Manifold Langevin and adaptive optimizers from the SGD literature. We compare
against VAEs by training like-for-like generative models using our technique
against those trained with standard reparameterisation-trick-based ELBOs. We
observe our method out-performs or matches performance across a number of
metrics, including sample quality, while converging in a fraction of the number
of SGD training iterations.

我们提出了一种新颖的算法，用于在通用深度生成模型中学习参数，该算法基于计算神经科学的预测编码 (PC) 框架。通过将高斯噪声注入 PC 推理过程，我们重新构想它作为过阻尼 Langevin 采样，从而实现对紧密证据下界 (ELBO) 的优化。我们通过将编码器网络纳入到我们的 Langevin 采样中，提供一个得到温和起步的改进的编码器自由训练方法，并测试了三种不同的目标。最后，为了增加对采样步长的鲁棒性，并减少对曲率的敏感性，我们验证了一种轻量级且易于计算的预条件形式，受到 Riemann 流形 Langevin 和 SGD 文献中的自适应优化器的启发。我们通过使用我们的技术和使用标准重参数化技巧的 ELBO 训练的模型进行训练，在许多指标上观察到我们的方法优于或与其性能相匹配，并且收敛迭代次数仅为其一部分。