Over the past decade, deep learning research has been accelerated by
increasingly powerful hardware, which facilitated rapid growth in the model
complexity and the amount of data ingested. This is becoming unsustainable and
therefore refocusing on efficiency is necessary. In this paper, we employ
transfer learning to improve training efficiency for large-scale spatial
problems. We propose that a convolutional neural network (CNN) can be trained
on small windows of signals, but evaluated on arbitrarily large signals with
little to no performance degradation, and provide a theoretical bound on the
resulting generalization error. Our proof leverages shift-equivariance of CNNs,
a property that is underexploited in transfer learning. The theoretical results
are experimentally supported in the context of mobile infrastructure on demand
(MID). The proposed approach is able to tackle MID at large scales with
hundreds of agents, which was computationally intractable prior to this work.

本文探讨了如何通过使用转移学习和卷积神经网络等技术来提高大规模空间问题的训练效率，提出小信号窗口训练成果可在大信号上应用的理论，并在移动基础设施方面得到了实证支持。