Training with Quantization Noise for Extreme Model Compression
https://arxiv.org/pdf/2004.07320.pdf 论文的核心思想如下： 针对上图进行实例分析： 如对卷积层weight为（64,64,3，3）进行量化 设置centroids数目如（32,9）其中9表示3*3卷积核的flatten 通过k-means进行选取centroids 之后进行编码（codebook），生成三个矩阵： centroids=（32,9） assignm
相关文章
- 1. Training with Quantization Noise for Extreme Model Compression
- 2. 神经网络参数量化，模型压缩论文笔记Training with Quantization Noise for Extreme Model Compression
- 3. Model Compression and Acceleration Overview
- 4. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 5. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
- 6. 论文阅读《A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising》CVPR2020
- 7. 【论文阅读笔记】Deep Neural Network Compression with Single and Multiple Level Quantization
- 8. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 9. Compression Deep Neural Networks With Pruning, Trained Quantization And Huffman Coding
- 10. 【2】Model Compression As Constrained Optimization, with Application to Neural Nets. 2017
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • 为了进字节跳动，我精选了29道Java经典算法题，带详细讲解
- • 算法总结-股票买卖
相关标签/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。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Training with Quantization Noise for Extreme Model Compression
- 2. 神经网络参数量化，模型压缩论文笔记Training with Quantization Noise for Extreme Model Compression
- 3. Model Compression and Acceleration Overview
- 4. Deep Compression : Compressing Deep Neual Networks With Pruning, Trained Quantization And Huffman Co
- 5. 2018-22Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And...
- 6. 论文阅读《A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising》CVPR2020
- 7. 【论文阅读笔记】Deep Neural Network Compression with Single and Multiple Level Quantization
- 8. 【Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huff】论文笔记
- 9. Compression Deep Neural Networks With Pruning, Trained Quantization And Huffman Coding
- 10. 【2】Model Compression As Constrained Optimization, with Application to Neural Nets. 2017