论文总结：Quantizing deep convolutional networks for efficient inference: A whitepaper
论文地址：https://arxiv.org/pdf/1806.08342.pdf 主要内容 这篇论文是篇介绍量化的综述，对量化的动机、方法等进行了较为详细的描述。 量化器的设计。作者介绍了三种量化方案：一致仿射量化器、均匀对称量化器和随机量化器。 模型量化的方法。大致可以分为两类：Post Training Quantization(训练后量化)和 Quantization Aware Trai
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相关文章
- 1. 论文笔记：Quantizing deep convolutional networks for efficient inference: A whitepaper
- 2. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 3. 论文阅读——ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 4. 论文翻译：ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 5. 【论文阅读笔记】ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 6. 论文《Convolutional Neural Networks for Sentence Classification》总结
- 7. VGG论文《Very Deep Convolutional Networks For Large Scale Image Recognition》总结
- 8. 论文阅读——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 9. 【论文笔记】Resolution Adaptive Networks for Efficient Inference （CVPR2020）
- 10. 【论文翻译】Fully Convolutional Networks for Semantic Segmentation_2