【模型性能1-泛化原因分析】On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
轉載 https://blog.csdn.net/zhangboshen/article/details/72853121 這是一篇發表在ICLR2017上面的文章。 這篇文章探究了深度學習中一個普遍存在的問題——使用大的batchsize訓練網絡會導致網絡的泛化性能下降（文中稱之爲Generalization Gap）。文中給出了Generalization Gap現象的解釋：大的batchsi
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相關文章
- 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相關理論與實踐
- 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-泛化產生】Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
- 7. 『 論文閱讀』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