Focusing on implicit neural representations, we present a novel in situ training protocol that employs limited memory buffers of full and sketched data samples, where the sketched data are leveraged to prevent catastrophic forgetting. The theoretical motivation for our use of sketching as a regularizer is presented via a simple Johnson-Lindenstrauss-informed result. While our methods may be of wider interest in the field of continual learning, we specifically target in situ neural compression using implicit neural representation-based hypernetworks. We evaluate our method on a variety of complex simulation data in two and three dimensions, over long time horizons, and across unstructured grids and non-Cartesian geometries. On these tasks, we show strong reconstruction performance at high compression rates. Most importantly, we demonstrate that sketching enables the presented in situ scheme to approximately match the performance of the equivalent offline method.

翻译：聚焦于隐式神经表示，我们提出了一种新颖的原位训练协议，该协议利用有限的内存缓冲区存储完整和草图化的数据样本，其中草图化数据被用于防止灾难性遗忘。通过一个简化的Johnson-Lindenstrauss相关结果，我们给出了将草图化作为正则化器的理论动机。尽管我们的方法可能在持续学习领域具有更广泛的兴趣，但本文专门针对基于隐式神经表示的超网络的神经压缩原位训练。我们在二维和三维的多种复杂模拟数据、长时间跨度、非结构化网格及非笛卡尔几何结构上评估了该方法。在这些任务中，我们展示了高压缩率下的强大重建性能。最重要的是，我们证明草图化使得所提出的原位方案能够近似达到等效离线方法的性能水平。