Adaptive Loss-aware Quantization for Multi-bit Networks
本文提出了一種通過減小量化誤差來獲得更好量化效果的自適應MBN(Multi-bit Networks)方法。 MBN方法，簡單來說，是基於這樣一個觀察：網絡中各層對量化粒度的敏感程度是不同的。那麼假設我們給予的總的bit數不變的基礎上，分別給對量化更敏感的層更多的bit數，較不敏感的層更少的bit數，從而達到更好的精度。 本文在這個思想上做出了改進：首先它優化的目標不是網絡的損失，而是引入的量化誤
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相關文章
- 1. Quantization Networks
- 2. Domain Adaptive Neural Networks for Object Recognition
- 3. Weighted-Entropy-based Quantization for Deep Neural Networks
- 4. Spatially Adaptive Residual Networks for Efficient Image and Video Deblurring
- 5. 《Resolution Adaptive Networks for Effificient Inference》閱讀筆記
- 6. Ternary Neural Networks with Fine-Grained Quantization
- 7. Adaptive Graph Convolutional Neural Networks
- 8. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 9. Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation-筆記
- 10. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference