Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
本文提出了一種可學習的量化間隔(Quantization Interval Learning,QIL)方法。 它的思路類似TSQ，都是分兩步進行量化，但與TSQ不同，它在每一步裏都同時對權重和激活值進行處理。 第一步是一個Transformer：其思路與DoReFa-Net類似，將權重變換到[-1,1]，將激活值變換到[0,1]。 其框架如下： 權重和激活值的變換公式分別爲： 這裏Cw和Cx分別爲
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相關文章
- 1. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 2. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 3. 通過訓練獲得最優量化區間——Quantization Interval Learning
- 4. Paper：Sequence to Sequence Learning with Neural Networks
- 5. Deep Learning with Torch
- 6. Learning to Track at 100 FPS with Deep Regression Networks
- 7. (轉) Learning Deep Learning with Keras
- 8. 【2017_ICCV】Deep Metric Learning with Angular Loss
- 9. 論文翻譯：Deep Learning with Low Precision by Half-wave Gaussian Quantization
- 10. learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization論文