Efficient model selection for identifying a suitable pre-trained neural
network to a downstream task is a fundamental yet challenging task in deep
learning. Current practice requires expensive computational costs in model
training for performance prediction. In this paper, we propose a novel
framework for neural network selection by analyzing the governing dynamics over
synaptic connections (edges) during training. Our framework is built on the
fact that back-propagation during neural network training is equivalent to the
dynamical evolution of synaptic connections. Therefore, a converged neural
network is associated with an equilibrium state of a networked system composed
of those edges. To this end, we construct a network mapping $\phi$, converting
a neural network $G_A$ to a directed line graph $G_B$ that is defined on those
edges in $G_A$. Next, we derive a neural capacitance metric $\beta_{\rm eff}$
as a predictive measure universally capturing the generalization capability of
$G_A$ on the downstream task using only a handful of early training results. We
carried out extensive experiments using 17 popular pre-trained ImageNet models
and five benchmark datasets, including CIFAR10, CIFAR100, SVHN, Fashion MNIST
and Birds, to evaluate the fine-tuning performance of our framework. Our neural
capacitance metric is shown to be a powerful indicator for model selection
based only on early training results and is more efficient than
state-of-the-art methods.

本文介绍了一种基于分析神经网络训练过程中连接权重的演化动力学来进行有效模型选择的新型框架，并提出了神经电容指标作为预测性能的度量标准。通过对 17 个广泛使用的预先训练的 ImageNet 模型和 5 个基准数据集的大量实验，验证了神经电容指标的有效性，相比现有方法更为高效。