Recent advances in hardware and big data acquisition have accelerated the development of deep learning techniques. For an extended period of time, increasing the model complexity has led to performance improvements for various tasks. However, this trend is becoming unsustainable and there is a need for alternative, computationally lighter methods. In this paper, we introduce a novel framework for efficient training of convolutional neural networks (CNNs) for large-scale spatial problems. To accomplish this we investigate the properties of CNNs for tasks where the underlying signals are stationary. We show that a CNN trained on small windows of such signals achieves a nearly performance on much larger windows without retraining. This claim is supported by our theoretical analysis, which provides a bound on the performance degradation. Additionally, we conduct thorough experimental analysis on two tasks: multi-target tracking and mobile infrastructure on demand. Our results show that the CNN is able to tackle problems with many hundreds of agents after being trained with fewer than ten. Thus, CNN architectures provide solutions to these problems at previously computationally intractable scales.

翻译：硬件和大数据采集的最新进展加速了深度学习技术的发展。在相当长的一段时间内，增加模型复杂度已为各种任务带来了性能提升。然而，这一趋势正变得不可持续，亟需寻找替代性的、计算量更轻的方法。本文提出了一种新颖的框架，用于高效训练面向大规模空间问题的卷积神经网络。为实现这一目标，我们研究了CNN在底层信号为平稳信号的任务中的特性。结果表明，在此类信号的小窗口上训练的CNN，无需重新训练即可在更大窗口上达到近乎相同的性能。这一论断得到了我们理论分析的支持，该分析给出了性能退化的上界。此外，我们在两个任务上进行了全面的实验分析：多目标跟踪与按需移动基础设施。实验结果显示，CNN在训练时仅使用少于十个智能体的情况下，便能处理包含数百个智能体的问题。因此，CNN架构为解决此类问题提供了此前在计算上难以实现的尺度解决方案。