This paper proposes a new type of generative model that is able to quickly
learn a latent representation without an encoder. This is achieved using
empirical Bayes to calculate the expectation of the posterior, which is
implemented by initialising a latent vector with zeros, then using the gradient
of the log-likelihood of the data with respect to this zero vector as new
latent points. The approach has similar characteristics to autoencoders, but
with a simpler architecture, and is demonstrated in a variational autoencoder
equivalent that permits sampling. This also allows implicit representation
networks to learn a space of implicit functions without requiring a
hypernetwork, retaining their representation advantages across datasets. The
experiments show that the proposed method converges faster, with significantly
lower reconstruction error than autoencoders, while requiring half the
parameters.

本文提出了一种新型生成模型，通过零初始化潜在向量，然后使用数据与该向量的对数似然梯度作为新的潜变量，实现经验贝叶斯法来计算后验期望，此法性能类似于自编码器，但是架构更简单，可以用于变分自编码器，可以通过隐式表示网络学习一组隐式函数空间，而不需要超网络，等等，实验表明，相对于自编码器，该方法收敛更快，重构误差显著更低，同时只需要一半的参数。