Quantization Networks
本文的創新點是，提出了了一種新的計算量化網絡梯度的方法。 在對網絡進行量化處理時，一個很常見的問題是，量化函數幾乎不可導，因此很難通過反向傳播來更新參數。爲了解決這個問題，人們提出了種種近似方法。但這些近似方法終究和量化函數有所區別，因此就會導致梯度的不匹配問題。 於是本文提出了一種新的量化函數： (這裏A是一個單位階躍函數，β是所有輸入值的放縮尺度，x是要被量化的權重或激活值，bi和si是階躍函
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相關文章
- 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