Generalization Generalization gap 指的是模型在训练集上表现和在测试集上表现的差距。深度学习模型的目标是要减小 generalization gap。 下面讲依次介绍这篇文章中发现的一些现象。 二、在有监督学习中 interference 和 generalization gap 正相关,但是在强化学习里面他们负相关 任务介绍: SVHN、CIFAR + Supervised 是标准...
* 作者认为generalization gap不是由于over-fitting(一个表达能力特别强的model在仅有的训练数据上过度训练,从而在某个迭代点达到test acc的峰值,然后又由于这个model在特定的训练集上的学习特性使test acc下降。 这在实验中没有发现,所以early-stopping启发式地防止过拟合不能减小generalization gap) ### Parametric p...
【模型性能1-泛化原因分析】On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima,程序员大本营,技术文章内容聚合第一站。
nature machine intelligence Volume 5 | December 2023 | 1340–1341 | 1340https://doi.org/10.1038/s42256-023-00766-7News & viewsNeural networksDeconstructing the generalization gapAndrey GromovNew research reveals a duality between neural network weights and neuron activities that enables a geometri...
New research reveals a duality between neural network weights and neuron activities that enables a geometric decomposition of the generalization gap. The framework provides a way to interpret the effects of regularization schemes such as stochastic gradient descent and dropout on generalization — and to...
Increasingly, external testing studies are being conducted that aim to bridge the existing generalization gap, and ultimately, the implementation gap (2). The goal is to establish standardized methodologies and guidelines that can help overcome the challenges associated with the limited applicability of ...
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima[J]. arXiv: Learning, 2016.作者代码@article{keskar2016on, title={On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima}, author={Keskar, Nitish Shirish and Mudigere, Dheevatsa and Nocedal,...
如何理解data dependence independence对generalization gap的影响 p12 如何在规范训练数据的基础上直接计算generalization gap来判断模型预测力? 忽略:在怎样的条件下,计算generalization gap不受weights数量,模型深度,input dimensions的影响 新开发的regularization公式的新颖之处在哪里?p13 ...
are well-calibrated. This perspective unifies many results in the literature, and suggests that interventions which reduce the generalization gap (such as adding data, using heavy augmentation, or smaller model size) also improve calibration. We thus hope our initial study lays the groundwork for ...
【模型性能1-泛化原因分析】On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima 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...