【MutualNet 】2020-ECCV oral-MutualNet Adaptive ConvNet via Mutual Learning from Network Width and Resolution-论文阅读

MutualNet

2020-ECCV oral-MutualNet Adaptive ConvNet via Mutual Learning from Network Width and Resolution

来源：ChenBong 博客园

• Institute：University of North Carolina at Charlotte
• Author：Taojiannan Yang、Chen Chen
• GitHub：https://github.com/taoyang1122/MutualNet 【】
• Citation： 5

Introduction

加入分辨率自适应的动态slim网络

Motivation

Contribution

Method

Framework

Model Training

有效性的一个解释

Final model

• Grid Search，query table

Experiments

ImageNet (compare with: US-Net)

compare with: individual model+multi resolution

Ablation Study

compare with: US-Net + multi resolution

• Grid Search，query table

compare with: Multi-scale Data Augmentation

MobileNet + Multi-scale data augmentation.

In multi-scale data augmentation, the network may take images of different resolutions in different iterations. But within each iteration, the network weights are optimized in the same resolution direction.

While our method randomly samples four sub-networks which share weights with each other.

the weights are optimized in a mixed resolution direction in each iteration.

US-Net + Multi-scale data augmentation

we randomly choose a scale from {224, 192, 160, 128} and feed the same scaled image to all sub-networks in each iteration.

the weights are still optimized towards a single resolution direction in each iteration, but the direction varies among different iterations.

Effects of Width Lower Bound

Boosting Single Network Performance

As discussed above, the performance of the full-network is greatly improved as we increase the width lower bound.

Conclusion

Summary

• 本质上就是在US-Net加了多个分辨率，并对不同规模的网络做了KD进行训练
• 实验比较充分

To Read

Reference