论文标题
通过循环学习率的准确性与培训时间的快速基准测试
Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates
论文作者
论文摘要
基于神经网络准确性和训练时间之间的权衡在计算上是昂贵的。在这里，我们展示了如何使用乘法循环学习率时间表来在单次训练中构建权衡曲线。我们生成了环状折衷曲线，用于训练方法的组合，例如Blurpool，最后的频道，标签平滑和混合，并突出显示如何使用这些环状折衷曲线来评估算法选择对网络训练效率的影响。
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive. Here we show how a multiplicative cyclic learning rate schedule can be used to construct a tradeoff curve in a single training run. We generate cyclic tradeoff curves for combinations of training methods such as Blurpool, Channels Last, Label Smoothing and MixUp, and highlight how these cyclic tradeoff curves can be used to evaluate the effects of algorithmic choices on network training efficiency.
