LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
ECCV-2018 ABSTRACT 虽然权重和激活值的量化是深度神经网络（DNN）压缩的有效方法，而且具备很大的潜力来提升利用位操做的推理速度，可是在量化模型和full precision模型之间的预测精度方面仍然存在明显的差距。为了弥补这个差距，咱们提出了对量化的，位操做兼容的DNN及其相关量化器的联合训练方法，而不是使用固定的手工量化方案，如均匀或对数量化。咱们学习量化器的方法适用于任意位精
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相关文章
- 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论文
- 6. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- 7. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 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