  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.
