Recent Neural Architecture Search (NAS) solutions have produced impressive
results training super-networks and then deriving subnetworks, a.k.a. child
models that outperform expert-crafted models from a pre-defined search space.
Efficient and robust subnetworks can be selected for resource-constrained edge
devices, allowing them to perform well in the wild. However, constructing
super-networks for arbitrary architectures is still a challenge that often
prevents the adoption of these approaches. To address this challenge, we
present BootstrapNAS, a software framework for automatic generation of
super-networks for NAS. BootstrapNAS takes a pre-trained model from a popular
architecture, e.g., ResNet- 50, or from a valid custom design, and
automatically creates a super-network out of it, then uses state-of-the-art NAS
techniques to train the super-network, resulting in subnetworks that
significantly outperform the given pre-trained model. We demonstrate the
solution by generating super-networks from arbitrary model repositories and
make available the resulting super-networks for reproducibility of the results.

本文描述了 BootstrapNAS，这是一个用于自动生成 NAS 超网络的软件框架，它基于预先训练的模型创建超网络，然后使用先进的 NAS 技术来训练超网络，生成有效的子网络，为资源受限的边缘设备提供高效解决方案。