We introduce an efficient optimization-based meta-learning technique for learning large-scale implicit neural representations (INRs). Our main idea is designing an online selection of context points, which can significantly reduce memory requirements for meta-learning in any established setting. By doing so, we expect additional memory savings which allows longer per-signal adaptation horizons (at a given memory budget), leading to better meta-initializations by reducing myopia and, more crucially, enabling learning on high-dimensional signals. To implement such context pruning, our technical novelty is three-fold. First, we propose a selection scheme that adaptively chooses a subset at each adaptation step based on the predictive error, leading to the modeling of the global structure of the signal in early steps and enabling the later steps to capture its high-frequency details. Second, we counteract any possible information loss from context pruning by minimizing the parameter distance to a bootstrapped target model trained on a full context set. Finally, we suggest using the full context set with a gradient scaling scheme at test-time. Our technique is model-agnostic, intuitive, and straightforward to implement, showing significant reconstruction improvements for a wide range of signals. Code is available at https://github.com/jihoontack/ECoP

针对大规模隐式神经表示（INRs）的学习，我们提出了一种高效的优化元学习技术，通过在线选择上下文点，可以在任何已知场景中大幅减少元学习的内存需求，实现上下文剪枝以及信号适应。此项技术提出了三个具有技术性新颖性的方案：context pruning 方案的提出，来自全局结构预测误差的适应性选择策略，以及测试时间梯度缩放方案，具有显著的重构改进。

基于误差上下文修剪的隐式神经表示元学习的高效实现