We introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context point selection. This is achieved by focusing each learning step on the subset of data with the highest expected immediate improvement in model quality, resulting in the almost instantaneous modeling of global structure and subsequent refinement of high-frequency details. We further improve the quality of our meta-learned initialization by introducing a bootstrap correction resulting in the minimization of any error introduced by reduced context sets while simultaneously mitigating the well-known myopia of optimization-based meta-learning. Finally, we show how gradient re-scaling at meta-test time allows the learning of extremely high-quality neural fields in significantly shortened optimization procedures. Our framework is model-agnostic, intuitive, straightforward to implement, and shows significant reconstruction improvements for a wide range of signals. We provide an extensive empirical evaluation on nine datasets across multiple multiple modalities, demonstrating state-of-the-art results while providing additional insight through careful analysis of the algorithmic components constituting our method. Code is available at https://github.com/jihoontack/GradNCP

翻译：我们提出了一种高效的基于优化的元学习技术，用于大规模神经场训练，通过自动化的在线上下文点选择实现显著的内存节省。该技术通过将每个学习步骤聚焦于模型中质量预期即时提升最高的数据子集，从而近乎瞬时地建立全局结构，并随后优化高频细节。为提升元学习初始化的质量，我们引入了一种自举校正方法，在最小化因缩减上下文集引入的误差的同时，缓解了基于优化的元学习众所周知的短视问题。最后，我们展示了元测试时的梯度重新缩放如何使学习过程在显著缩短的优化步骤中生成极高品质的神经场。我们的框架与模型无关，直观且易于实现，在多种信号上展现出显著的重建性能提升。我们基于九个跨多种模态的数据集进行了广泛的实验评估，通过仔细分析构成方法的算法组件，在取得最先进结果的同时提供了额外见解。代码见 https://github.com/jihoontack/GradNCP。