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5.4 KiB
5.4 KiB
Summary
A Dual Path Network (DPN) is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that ResNets enables feature re-usage while DenseNet enables new feature exploration, and both are important for learning good representations. To enjoy the benefits from both path topologies, Dual Path Networks share common features while maintaining the flexibility to explore new features through dual path architectures.
The principal building block is an DPN Block.
{% include 'code_snippets.md' %}
How do I train this model?
You can follow the timm recipe scripts for training a new model afresh.
Citation
@misc{chen2017dual,
title={Dual Path Networks},
author={Yunpeng Chen and Jianan Li and Huaxin Xiao and Xiaojie Jin and Shuicheng Yan and Jiashi Feng},
year={2017},
eprint={1707.01629},
archivePrefix={arXiv},
primaryClass={cs.CV}
}