Quantization Networks
本文的创新点是，提出了了一种新的计算量化网络梯度的方法。 在对网络进行量化处理时，一个很常见的问题是，量化函数几乎不可导，因此很难通过反向传播来更新参数。为了解决这个问题，人们提出了种种近似方法。但这些近似方法终究和量化函数有所区别，因此就会导致梯度的不匹配问题。 于是本文提出了一种新的量化函数： (这里A是一个单位阶跃函数，β是所有输入值的放缩尺度，x是要被量化的权重或激活值，bi和si是阶跃函
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相关文章
- 1. Ternary Neural Networks with Fine-Grained Quantization
- 2. Weighted-Entropy-based Quantization for Deep Neural Networks
- 3. Adaptive Loss-aware Quantization for Multi-bit Networks
- 4. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 5. Stochastic Computing + Quantization
- 6. 【TensorFlow】quantization量化
- 7. Product Quantization
- 8. Iterative Quantization
- 9. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
- 10. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference