DOREFA-NET: TRAINING LOW BITWIDTH CONVOLUTIONAL NEURAL NETWORKS WITH LOW BITWIDTH GRADIENTS
BNN和异或网将权重和卷积层的值进行量化，进而将在前相传播过程中花费最多的卷积操作转化为了两个bit向量的逐比特的点积： 这里bitcount计算比特向量中比特的数量。 之前的网络都没能在反向传播保持8比特以下的精度的同时，能够拥有可接受的精度。 dorefa-net的创新点在于： 1、它能够以任意的精度量化权重、激活层和梯度。 2、由于比特卷积可以高效地在各种设备上实现，因此他为在各种软件上实现
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相关文章
- 1. 量化网络训练--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
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- 9. 论文阅读笔记（一）：Learning Dual Convolutional Neural Networks for Low-Level Vision
- 10. 论文阅读（【CVPR2018】Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision）