We present a simple and general method to train a single neural network
executable at different widths (number of channels in a layer), permitting
instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of
training individual networks with different width configurations, we train a
shared network with switchable batch normalization. At runtime, the network can
adjust its width on the fly according to on-device benchmarks and resource
constraints, rather than downloading and offloading different models. Our
trained networks, named slimmable neural networks, achieve similar (and in many
cases better) ImageNet classification accuracy than individually trained models
of MobileNet v1, MobileNet v2, ShuffleNet and ResNet-50 at different widths
respectively. We also demonstrate better performance of slimmable models
compared with individual ones across a wide range of applications including
COCO bounding-box object detection, instance segmentation and person keypoint
detection without tuning hyper-parameters. Lastly we visualize and discuss the
learned features of slimmable networks. Code and models are available at:
this https URL

本文介绍了一种简单通用的方法去训练单个神经网络的可执行不同宽度（即层中通道的数量），从而允许在运行时进行即时和自适应的准确性和效率权衡。基于共享网络的可切换批量归一化，可以在不同的宽度配置下进行训练，并可根据实时运行结果和资源限制在多个应用领域中实现类似甚至更好的准确性和效率表现。