論文閱讀——AdderNet: Do We Really Need Multiplications in Deep Learning?
論文閱讀之AdderNet：深度學習中真的需要乘法嗎？ AdderNet Code CVPR2020 from 北大&華爲諾亞實驗室 Abstract 與加法運算相比，乘法運算更加複雜。而在神經網絡中廣泛使用的卷積操作主要就是通過大量的浮點乘法來進行輸入圖像與filter之間的互相關運算。本文提出的AdderNet則通過加法而不是乘法來減少計算上的開銷。AdderNet中將輸入與各filter之間
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- 4. 論文閱讀：Do We Really Need Multiplications in Deep Learning?
- 5. AdderNet: Do we really need multiplications in deep learning——準研究生論文週報
- 6. 論文筆記：Do We Really Need Multiplications in Deep Learning？
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相關文章
- 1. 論文閱讀——AdderNet: Do We Really Need Multiplications in Deep Learning?
- 2. 論文筆記：AdderNet: Do We Really Need Multiplications in Deep Learning?
- 3. Addernet學習筆記 --- AdderNet: Do We Really Need Multiplications in Deep Learning?
- 4. 論文閱讀：Do We Really Need Multiplications in Deep Learning?
- 5. AdderNet: Do we really need multiplications in deep learning——準研究生論文週報
- 6. 論文筆記：Do We Really Need Multiplications in Deep Learning？
- 7. 【Paper Reading】AdderNet: DoWe Really Need Multiplications in Deep Learning?
- 8. 加法神經網絡--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?