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

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

27 Sep 2019ECCV 2020 8arXiv:1909.12978archive 2025-07-28

Taojiannan Yang, Sijie Zhu, Chen Chen, Shen Yan, Mi Zhang, Andrew Willis

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}.

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Instance Segmentation Object Detection Semantic Segmentation Transfer Learning object-detection

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1x1 Convolution Average Pooling Batch Normalization Convolution Dense Connections Depthwise Convolution Depthwise Separable Convolution Dropout EfficientNet Inverted Residual Block Pointwise Convolution RMSProp ReLU Sigmoid Activation Squeeze-and-Excitation Block

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