本文汇总了多种量化神经网络的研究成果，包括BNN、XNOR-Net、DOREFA-NET、ABC-Net等，探讨了不同量化方法对网络性能的影响及优化策略，为高效推理提供了理论基础。
1.【论文笔记】BNN
引用：https://blog.youkuaiyun.com/elaine_bao/article/details/50950969
2.【论文笔记】XNOR-Net
引用：https://blog.youkuaiyun.com/u014380165/article/details/77731595
3.【论文笔记】DOREFA-NET
https://blog.youkuaiyun.com/u010333076/article/details/87897408
4.【论文笔记】ABC-Net
https://blog.youkuaiyun.com/u010333076/article/details/87897438
5.【论文笔记】Ternary weight networks
https://blog.youkuaiyun.com/xjtu_noc_wei/article/details/52862282
6.【论文笔记】incremental network quantization
https://blog.youkuaiyun.com/cookie_234/article/details/75386737
7.【论文笔记】fine-grained quantization
https://blog.youkuaiyun.com/yingpeng_zhong/article/details/80548183
8.【论文笔记】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
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