Deep neural networks achieve remarkable performance in many computer vision
tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection
approaches reuse neural network architectures designed for image classification
as the backbone, commonly pre-trained on ImageNet. However, performance gains
can be achieved by designing network architectures specifically for detection
and segmentation, as shown by recent neural architecture search (NAS) research
for detection and segmentation. One major challenge though, is that ImageNet
pre-training of the search space representation (a.k.a. super network) or the
searched networks incurs huge computational cost. In this paper, we propose a
Fast Neural Network Adaptation (FNA) method, which can adapt both the
architecture and parameters of a seed network (e.g. a high performing manually
designed backbone) to become a network with different depth, width, or kernels
via a Parameter Remapping technique, making it possible to utilize NAS for
detection/segmentation tasks a lot more efficiently. In our experiments, we
conduct FNA on MobileNetV2 to obtain new networks for both segmentation and
detection that clearly out-perform existing networks designed both manually and
by NAS. The total computation cost of FNA is significantly less than SOTA
segmentation/detection NAS approaches: 1737$\times$ less than DPC, 6.8$\times$
less than Auto-DeepLab and 7.4$\times$ less than DetNAS. The code is available
at this https URL

本文提出了基于参数重映射技术的 Fast Neural Network Adaptation (FNA) 方法，该方法可以将已有的高性能网络结构调整为不同的深度、宽度或内核大小，进而实现更高效的神经架构搜索，该方法在 MobileNetV2 的实验中，通过对现有网络进行 FNA 调整，既可用于目标分割任务又可用于目标检测任务，并且在性能上均超过了手动设计和 NAS 的现有网络设计，且其计算成本明显低于 SOTA 的目标分割 /detection NAS 方法 (DPC、Auto-DeepLab 和 DetNAS）。