  Neural fields, which represent signals as a function parameterized by a
neural network, are a promising alternative to traditional discrete vector or
grid-based representations. Compared to discrete representations, neural
representations both scale well with increasing resolution, are continuous, and
can be many-times differentiable. However, given a dataset of signals that we
would like to represent, having to optimize a separate neural field for each
signal is inefficient, and cannot capitalize on shared information or
structures among signals. Existing generalization methods view this as a
meta-learning problem and employ gradient-based meta-learning to learn an
initialization which is then fine-tuned with test-time optimization, or learn
hypernetworks to produce the weights of a neural field. We instead propose a
new paradigm that views the large-scale training of neural representations as a
part of a partially-observed neural process framework, and leverage neural
process algorithms to solve this task. We demonstrate that this approach
outperforms both state-of-the-art gradient-based meta-learning approaches and
hypernetwork approaches.
