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。这是通过将每个学习步骤聚焦于对模型质量具有最高预期即时改进的数据子集来实现的，从而几乎即时地对全局结构进行建模，并随后细化高频细节。我们进一步通过引入自举校正来改进元学习初始化的质量，从而在最小化由缩减上下文集引入的任何误差的同时，缓解了基于优化的元学习众所周知的短视问题。最后，我们展示了在元测试时进行梯度重新缩放如何能够在显著缩短的优化过程中学习到极高品质的神经场。我们的框架是模型无关的、直观且易于实现的，并且对广泛的信号显示出显著的 reconstruction 改进。我们在九个跨多种模态的数据集上进行了广泛的实证评估，展示了最先进的结果，同时通过对我们方法组成的算法组件进行仔细分析提供了额外的见解。代码可在 https://github.com/jihoontack/GradNCP 获取。