【Pruning系列：一】Learning Efficient Convolutional Networks through Network Slimming

时间 2021-01-02

标签 Model Compression 计算机视觉深度学习神经网络卷积网络栏目系统网络繁體版

原文原文链接

NS 机构 Tsinghua,Intel motivation 训练中的剪枝基于BN（Batch Normalization）层的广泛使用，在BN层加入channel-wise scaling factor 并对之加L1 regularizer使之稀疏，然后裁剪scaling factor值小的部分对应权重用 BN 层的 y 来表示卷积核的重要程度，y 小对应卷积核重要性低 γ 非常小时，送入

>>阅读原文<<

1. 模型压缩：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
2. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3实践｜Pytorch 总结
3. 模型压缩之Learning Efficient Convolutional Networks through Network Slimming
4. 《Learning Efficient Convolutional Networks through Network Slimming》论文笔记
5. 【论文阅读笔记】Learning Efficient Convolutional Networks through Network Slimming
6. DL经典文章翻译2：Learning Efficient Convolutional Networks through Network Slimming
7. [论文理解] Learning Efficient Convolutional Networks through Network Slimming
8. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
9. Pruning Convolutional Neural Networks For Resource Efficient Inference
10. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
更多相关文章...
• Hibernate一对多映射关系 - Hibernate教程
• Scala List(列表) - Scala教程
• Docker容器实战(七) - 容器眼光下的文件系统
• RxJava操作符（一）Creating Observables

最新文章

1. 说说Python中的垃圾回收机制？
2. 蚂蚁金服面试分享，阿里的offer真的不难，3位朋友全部offer
3. Spring Boot （三十一）——自定义欢迎页及favicon
4. Spring Boot核心架构
5. IDEA创建maven web工程
6. 在IDEA中利用maven创建java项目和web项目
7. myeclipse新导入项目基本配置
8. zkdash的安装和配置
9. 什么情况下会导致Python内存溢出？要如何处理？
10. CentoOS7下vim输入中文

本站公众号

欢迎关注本站公众号,获取更多信息

1. 模型压缩：Networks Slimming-Learning Efficient Convolutional Networks through Network Slimming
2. 【Pruning系列：二】Learning Efficient Convolutional Networks through Network Slimming｜YOLOv3实践｜Pytorch 总结
3. 模型压缩之Learning Efficient Convolutional Networks through Network Slimming
4. 《Learning Efficient Convolutional Networks through Network Slimming》论文笔记
5. 【论文阅读笔记】Learning Efficient Convolutional Networks through Network Slimming
6. DL经典文章翻译2：Learning Efficient Convolutional Networks through Network Slimming
7. [论文理解] Learning Efficient Convolutional Networks through Network Slimming
8. 模型剪枝----Learning Efficient Convolutional Networks through Network Slimming
9. Pruning Convolutional Neural Networks For Resource Efficient Inference
10. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning

>>更多相关文章<<