2019 CVPR: A main/subsidiary network framework for simplifying binary neural networks
這是一篇2019年的CVPR，其工作十分有趣！筆者下面帶大家簡單地看看這篇文章： 論文地址：https://arxiv.org/abs/1812.04210 1、摘要 模型壓縮研究領域中有兩種熱門的方法：量化和剪枝，這些都在我的博客中講過，特別是剪枝。在量化過程中，有一種特別極端的方法，1bit的二值量化。對於一個二值網絡而言，其本身就是「壓縮」的。這篇文章致力於用其提出的一種bottom-up和
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相關文章
- 1. （轉）A Recipe for Training Neural Networks
- 2. Balanced Binary Neural Networks With Gated Residual
- 3. Forward and Backward Information Retention for Accurate Binary Neural Networks
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- 5. [CVPR 2020] 3DRegNet: A Deep Neural Network for 3D Point Registration
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