DOREFA-NET: TRAINING LOW BITWIDTH CONVOLUTIONAL NEURAL NETWORKS WITH LOW BITWIDTH GRADIENTS
BNN和異或網將權重和卷積層的值進行量化，進而將在前相傳播過程中花費最多的卷積操作轉化爲了兩個bit向量的逐比特的點積： 這裏bitcount計算比特向量中比特的數量。 之前的網絡都沒能在反向傳播保持8比特以下的精度的同時，能夠擁有可接受的精度。 dorefa-net的創新點在於： 1、它能夠以任意的精度量化權重、激活層和梯度。 2、由於比特卷積可以高效地在各種設備上實現，因此他爲在各種軟件上實現
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相關文章
- 1. 量化網絡訓練--Towards Effective Low-bitwidth Convolutional Neural Networks
- 2. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
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- 4. 《SWALP：Stochastic Weight Averaging in Low-Precision Training》
- 5. LCDet Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
- 6. Paper reading (四十二)：Low Data Drug Discovery with One-Shot Learning
- 7. Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
- 8. 論文閱讀：Learning Dual Convolutional Neural Networks for Low-Level Vision
- 9. 論文閱讀筆記（一）：Learning Dual Convolutional Neural Networks for Low-Level Vision
- 10. 論文閱讀（【CVPR2018】Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision）