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.

翻译：神经场将信号表示为神经网络参数化的函数，是传统离散向量或网格表示方法的一种有前景的替代方案。与离散表示相比，神经表示不仅能够随着分辨率提升良好扩展，还具有连续性和多次可微性。然而，当面对一组需要表示的信号数据集时，为每个信号单独优化一个神经场效率低下，且无法利用信号间的共享信息或结构。现有泛化方法将这一问题视为元学习任务，采用基于梯度的元学习来学习初始化参数，再通过测试时优化进行微调，或者学习超网络以生成神经场的权重。我们转而提出一种新范式，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此任务。实验证明，该方法在性能上优于当前最先进的基于梯度的元学习方法以及超网络方法。