【模型性能1-泛化原因分析】On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

时间 2021-01-02

标签机器学习繁體版

原文原文链接

转载 https://blog.csdn.net/zhangboshen/article/details/72853121 这是一篇发表在ICLR2017上面的文章。这篇文章探究了深度学习中一个普遍存在的问题——使用大的batchsize训练网络会导致网络的泛化性能下降（文中称之为Generalization Gap）。文中给出了Generalization Gap现象的解释：大的batchsi

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1. ON LARGE BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA
2. Understanding deep learning requires rethinking generalization
3. Deep Learning中的Large Batch Training相关理论与实践
4. Wide and Deep Learning（1）原理
5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
6. 【模型性能2-泛化产生】Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
7. 『论文阅读』Understanding deep learning requires rethinking generalization
8. Wide & Deep Learning for Recommender Systems
9. Paper Reading:Wide & Deep Learning for Recommender Systems
10. Tips for Training Deep Neural Network
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1. ON LARGE BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA
2. Understanding deep learning requires rethinking generalization
3. Deep Learning中的Large Batch Training相关理论与实践
4. Wide and Deep Learning（1）原理
5. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation解析(PointNet模型)
6. 【模型性能2-泛化产生】Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
7. 『论文阅读』Understanding deep learning requires rethinking generalization
8. Wide & Deep Learning for Recommender Systems
9. Paper Reading:Wide & Deep Learning for Recommender Systems
10. Tips for Training Deep Neural Network

>>更多相关文章<<