In the past years, significant improvements in the field of neural
architecture search(NAS) have been made. However, it is still challenging to
search for efficient networks due to the gap between the searched constraint
and real inference time exists. To search for a high-performance network with
low inference time, several previous works set a computational complexity
constraint for the search algorithm. However, many factors affect the speed of
inference(e.g., FLOPs, MACs). The correlation between a single indicator and
the latency is not strong. Currently, some re-parameterization(Rep) techniques
are proposed to convert multi-branch to single-path architecture which is
inference-friendly. Nevertheless, multi-branch architectures are still
human-defined and inefficient. In this work, we propose a new search space that
is suitable for structural re-parameterization techniques. RepNAS, a one-stage
NAS approach, is present to efficiently search the optimal diverse branch
block(ODBB) for each layer under the branch number constraint. Our experimental
results show the searched ODBB can easily surpass the manual diverse branch
block(DBB) with efficient training.

本文提出一种新的搜索空间并使用 RepNAS 方法在分支数量约束下为每个层搜索最佳的多元分支块 (ODBB) 以实现高性能网络的搜索。实验结果表明，搜索到的 ODBB 可以轻松超越人工设计的多样分支块 (DBB)，并具有有效的训练。