论文阅读——AdderNet: Do We Really Need Multiplications in Deep Learning?
论文阅读之AdderNet：深度学习中真的须要乘法吗？ AdderNet Code CVPR2020 from 北大&华为诺亚实验室git Abstract 与加法运算相比，乘法运算更加复杂。而在神经网络中普遍使用的卷积操做主要就是经过大量的浮点乘法来进行输入图像与filter之间的互相关运算。本文提出的AdderNet则经过加法而不是乘法来减小计算上的开销。AdderNet中将输入与各filte
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相关文章
- 1. 论文阅读——AdderNet: Do We Really Need Multiplications in Deep Learning?
- 2. 论文笔记：AdderNet: Do We Really Need Multiplications in Deep Learning?
- 3. Addernet学习笔记 --- AdderNet: Do We Really Need Multiplications in Deep Learning?
- 4. 论文阅读：Do We Really Need Multiplications in Deep Learning?
- 5. AdderNet: Do we really need multiplications in deep learning——准研究生论文周报
- 6. 论文笔记：Do We Really Need Multiplications in Deep Learning？
- 7. 【Paper Reading】AdderNet: DoWe Really Need Multiplications in Deep Learning?
- 8. 加法神经网络--AdderNet: DoWe Really Need Multiplications in Deep Learning?
- 9. Do Deep Nets Really Need to be Deep?
- 10. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?