论文标题
利用过多的资源培训神经网络
Utilizing Excess Resources in Training Neural Networks
论文作者
论文摘要
在这项工作中，我们建议内核过滤线性过度参数化（KFLO），其中在训练过程中使用了线性过滤层的线性级联，以提高测试时间的网络性能。我们以内核过滤的方式实施了此级联反应，从而防止训练有素的建筑变得不必要。这也允许在几乎所有网络体系结构中使用我们的方法，并在测试时间将过滤层组合到单层中。因此，我们的方法在推断过程中不会增加计算复杂性。我们证明了KFLO在各种网络模型和数据集中的优势在监督学习中。
In this work, we suggest Kernel Filtering Linear Overparameterization (KFLO), where a linear cascade of filtering layers is used during training to improve network performance in test time. We implement this cascade in a kernel filtering fashion, which prevents the trained architecture from becoming unnecessarily deeper. This also allows using our approach with almost any network architecture and let combining the filtering layers into a single layer in test time. Thus, our approach does not add computational complexity during inference. We demonstrate the advantage of KFLO on various network models and datasets in supervised learning.
