论文标题
Vinnpruner：视觉互动式修剪深度学习
ViNNPruner: Visual Interactive Pruning for Deep Learning
论文作者
论文摘要
神经网络的规模大大增长，可以解决更复杂的任务。在许多情况下，如此大的网络无法在特定的硬件上部署，需要缩小尺寸。修剪技术只会尽可能少地降低其性能，从而有助于将深层神经网络缩小到较小的大小。但是，这种修剪算法通常很难通过应用它们来理解，并且不包括可能对用户目标不利的领域知识。我们提出了Vinnpruner，这是一种视觉交互式的修剪应用程序，可实现最先进的修剪算法，并根据用户根据知识进行手动修剪的选项。我们展示了该应用程序如何促进对自动修剪算法和半自动修剪超大网络的见解，从而使它们使用交互式可视化更有效。
Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep neural networks to smaller sizes by only decreasing their performance as little as possible. However, such pruning algorithms are often hard to understand by applying them and do not include domain knowledge which can potentially be bad for user goals. We propose ViNNPruner, a visual interactive pruning application that implements state-of-the-art pruning algorithms and the option for users to do manual pruning based on their knowledge. We show how the application facilitates gaining insights into automatic pruning algorithms and semi-automatically pruning oversized networks to make them more efficient using interactive visualizations.
