Convolutional neural networks (CNN) have become the predominant model for
image segmentation tasks. Most CNN segmentation architectures resize spatial
dimensions by a fixed factor of two to aggregate spatial context. Recent work
has explored using other resizing factors to improve model accuracy for
specific applications. However, finding the appropriate rescaling factor most
often involves training a separate network for many different factors and
comparing the performance of each model. The computational burden of these
models means that in practice it is rarely done, and when done only a few
different scaling factors are considered.
In this work, we present a hypernetwork strategy that can be used to easily
and rapidly generate the Pareto frontier for the trade-off between accuracy and
efficiency as the rescaling factor varies. We show how to train a single
hypernetwork that generates CNN parameters conditioned on a rescaling factor.
This enables a user to quickly choose a rescaling factor that appropriately
balances accuracy and computational efficiency for their particular needs. We
focus on image segmentation tasks, and demonstrate the value of this approach
across various domains. We also find that, for a given rescaling factor, our
single hypernetwork outperforms CNNs trained with fixed rescaling factors.

本文介绍了一种新的超网络策略可以用来快速生成卷积神经网络，从而用于处理图像分割任务，在尽可能地保证准确性的前提下提高计算效率，并成功展现了各种域的价值。