MutualNet：通过相互学习从网络宽度和分辨率进行自适应的ConvNet

我们提出了宽度分辨率相互学习方法（MutualNet），以训练可在动态资源约束下执行的网络，从而在运行时实现自适应精度-效率的折衷。我们的方法使用不同的输入分辨率训练一组具有不同宽度的子网，以相互学习每个子网的多尺度表示。..

MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution

We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with different widths using different input resolutions to mutually learn multi-scale representations for each sub-network.It achieves consistently better ImageNet top-1 accuracy over the state-of-the-art adaptive network US-Net under different computation constraints, and outperforms the best compound scaled MobileNet in EfficientNet by 1.5%. The superiority of our method is also validated on COCO object detection and instance segmentation as well as transfer learning. Surprisingly, the training strategy of MutualNet can also boost the performance of a single network, which substantially outperforms the powerful AutoAugmentation in both efficiency (GPU search hours: 15000 vs. 0) and accuracy (ImageNet: 77.6% vs. 78.6%). Code is available at \url{https://github.com/taoyang1122/MutualNet}.