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.

神经场是一种有望替代传统的离散向量或基于网格的表示方法的信号表示方法，通过神经网络将信号表示为一个函数的参数化形式。相比离散表示方法，神经表示方法在分辨率增加时具有更好的可扩展性，是连续的，而且可以多次可微。然而，对于我们想要表示的信号数据集，为每个信号优化一个单独的神经场效率低下，并且无法利用信号之间的共享信息或结构。现有的泛化方法将其视为元学习问题，并使用基于梯度的元学习来学习初始化，然后通过测试时优化进行微调，或者学习超网络来产生神经场的权重。我们提出了一种新的范式，将神经表示的大规模训练视为部分观测神经过程框架的一部分，并利用神经过程算法来解决这个任务。我们证明了这种方法优于最先进的基于梯度的元学习方法和超网络方法。