Implicit neural representations have emerged as a powerful technique for encoding complex continuous multidimensional signals as neural networks, enabling a wide range of applications in computer vision, robotics, and geometry. While Adam is commonly used for training due to its stochastic proficiency, it entails lengthy training durations. To address this, we explore alternative optimization techniques for accelerated training without sacrificing accuracy. Traditional second-order optimizers like L-BFGS are suboptimal in stochastic settings, making them unsuitable for large-scale data sets. Instead, we propose stochastic training using curvature-aware diagonal preconditioners, showcasing their effectiveness across various signal modalities such as images, shape reconstruction, and Neural Radiance Fields (NeRF).

翻译：隐式神经表示已成为一种强大的技术，能够将复杂的连续多维信号编码为神经网络，广泛应用于计算机视觉、机器人学和几何学领域。尽管Adam因其随机训练特性常被用于训练，但其训练时间较长。为此，我们探索了替代优化技术，以在不牺牲精度的前提下加速训练。传统的二阶优化器（如L-BFGS）在随机设置中表现欠佳，不适用于大规模数据集。我们转而提出使用曲率感知的对角预条件器进行随机训练，并展示了其在图像、形状重建和神经辐射场（NeRF）等多种信号模态中的有效性。