【论文阅读】Gradient Centralization: A New Optimization Technique for Deep Neural Networks

时间 2020-12-24

标签深度学习神经网络人工智能繁體版

原文原文链接

香港理工&阿里达摩院新作，操作优雅，一行代码嵌入优化器提升性能！ ArXiv 链接：https://arxiv.org/abs/2004.01461 Github 链接：https://github.com/Yonghongwei/Gradient-Centralization 一、摘要优化技术对有效地训练深度神经网络 (DNN) 而言具有重要意义。结果表明，利用一阶和二阶统计量 (如均值

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1. 【论文阅读】Gradient Centralization: A New Optimization Technique for Deep Neural Networks
2. Gradient Centralization: A New Optimization Technique for Deep Neural Networks（讲解）
3. 论文阅读：Deep Neural Networks for Object Detection
4. [论文解读] Concolic Testing for Deep Neural Networks
5. 【论文阅读】[2009.00278v2]Scaling Up Deep Neural Network Optimization for Edge Inference
6. 论文阅读-Fast Algorithms for Convolutional Neural Networks
7. 论文阅读：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
8. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
9. 论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
10. [论文阅读]ResNetXt|Aggregated Residual Transformations for Deep Neural Networks
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1. 【论文阅读】Gradient Centralization: A New Optimization Technique for Deep Neural Networks
2. Gradient Centralization: A New Optimization Technique for Deep Neural Networks（讲解）
3. 论文阅读：Deep Neural Networks for Object Detection
4. [论文解读] Concolic Testing for Deep Neural Networks
5. 【论文阅读】[2009.00278v2]Scaling Up Deep Neural Network Optimization for Edge Inference
6. 论文阅读-Fast Algorithms for Convolutional Neural Networks
7. 论文阅读：Aggregated Residual Transformations for Deep Neural Networks（ResNeXt）
8. 论文阅读：《Convolutional Neural Networks for Sentence Classification》
9. 论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
10. [论文阅读]ResNetXt|Aggregated Residual Transformations for Deep Neural Networks

>>更多相关文章<<