LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
ECCV-2018 ABSTRACT 雖然權重和激活值的量化是深度神經網絡（DNN）壓縮的有效方法，而且具備很大的潛力來提升利用位操做的推理速度，可是在量化模型和full precision模型之間的預測精度方面仍然存在明顯的差距。爲了彌補這個差距，咱們提出了對量化的，位操做兼容的DNN及其相關量化器的聯合訓練方法，而不是使用固定的手工量化方案，如均勻或對數量化。咱們學習量化器的方法適用於任意位精
相關文章
- 1. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 2. Weighted-Entropy-based Quantization for Deep Neural Networks
- 3. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 4. Forward and Backward Information Retention for Accurate Binary Neural Networks
- 5. learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization論文
- 6. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- 7. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 8. Neural Networks and Deep Learning
- 9. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
- 10. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 更多相關文章...
- • PHP compact() 函數 - PHP參考手冊
- • Swift for 循環 - Swift 教程
- • RxJava操作符（七）Conditional and Boolean
- • 算法總結-股票買賣
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
- 2. Weighted-Entropy-based Quantization for Deep Neural Networks
- 3. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 4. Forward and Backward Information Retention for Accurate Binary Neural Networks
- 5. learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization論文
- 6. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- 7. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】論文筆記
- 8. Neural Networks and Deep Learning
- 9. deep compression:compressing deep neural networks with pruning,trained quantization and huffman codi
- 10. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co