learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization論文
SQ-B(T)WN也是一種增量式量化算法，它從不同權重會導致不同量化誤差角度，在近似實數權重時，逐漸增加量化比例最終完成全部權重量化。 訓練時分階段對權重量化，都是瞄準量化造成誤差的一個量化時機問題 通過逐步量化權重，比直接全部量化產生的更加合適梯度方向 從算法結果上來說，看起來並不是特別好。3bits 時沒有同樣增量訓練的INQ好
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相關文章
- 1. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 2. Stochastic Computing + Quantization
- 3. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 4. Neural Networks and Deep Learning-引論
- 5. 論文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 6. Ternary Neural Networks with Fine-Grained Quantization
- 7. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 8. Few-Shot Learning with Graph Neural Networks
- 9. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
- 10. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi