Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works have explored the use of NeFs as representations for downstream tasks, e.g. classifying an image based on the parameters of a NeF that has been fit to it. However, the impact of the NeF hyperparameters on their quality as downstream representation is scarcely understood and remains largely unexplored. This is in part caused by the large amount of time required to fit datasets of neural fields. In this work, we propose $\verb|fit-a-nef|$, a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets, resulting in a significant speed-up. With this library, we perform a comprehensive study that investigates the effects of different hyperparameters -- including initialization, network architecture, and optimization strategies -- on fitting NeFs for downstream tasks. Our study provides valuable insights on how to train NeFs and offers guidance for optimizing their effectiveness in downstream applications. Finally, based on the proposed library and our analysis, we propose Neural Field Arena, a benchmark consisting of neural field variants of popular vision datasets, including MNIST, CIFAR, variants of ImageNet, and ShapeNetv2. Our library and the Neural Field Arena will be open-sourced to introduce standardized benchmarking and promote further research on neural fields.

翻译：神经场（NeFs）最近作为一种通用方法出现，用于建模不同模态的信号，包括图像、形状和场景。随后，多项工作探索了将NeFs作为下游任务表示的使用，例如，基于已拟合到图像的NeF参数对图像进行分类。然而，NeF超参数对其作为下游表示质量的影响尚不明确，且在很大程度上仍未得到探索。这部分是由于拟合神经场数据集所需的大量时间。在本工作中，我们提出了 $\verb|fit-a-nef|$，一个基于JAX的库，该库利用并行化实现大规模NeF数据集的快速优化，从而显著提升速度。借助该库，我们进行了一项全面研究，探讨了不同超参数——包括初始化、网络架构和优化策略——对拟合下游任务NeFs的影响。我们的研究为如何训练NeFs提供了宝贵见解，并为优化其在下游应用中的有效性提供了指导。最后，基于所提出的库和我们的分析，我们提出了神经场竞技场（Neural Field Arena），一个由流行视觉数据集的神经场变体组成的基准，包括MNIST、CIFAR、ImageNet的变体以及ShapeNetv2。我们的库和神经场竞技场将开源，以引入标准化基准测试并促进神经场的进一步研究。