  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 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 on fitting NeFs for
downstream tasks. In particular, we explore the use of a shared initialization,
the effects of overtraining, and the expressiveness of the network
architectures used. 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.
