The focus of this paper is speeding up the evaluation of convolutional neural
networks. While delivering impressive results across a range of computer vision
and machine learning tasks, these networks are computationally demanding,
limiting their deployability. Convolutional layers generally consume the bulk
of the processing time, and so in this work we present two simple schemes for
drastically speeding up these layers. This is achieved by exploiting
cross-channel or filter redundancy to construct a low rank basis of filters
that are rank-1 in the spatial domain. Our methods are architecture agnostic,
and can be easily applied to existing CPU and GPU convolutional frameworks for
tuneable speedup performance. We demonstrate this with a real world network
designed for scene text character recognition, showing a possible 2.5x speedup
with no loss in accuracy, and 4.5x speedup with less than 1% drop in accuracy,
still achieving state-of-the-art on standard benchmarks.

本文介绍了两种简单的方案，利用交叉通道或过滤器冗余构建低秩滤波器基，从而显著加速卷积层。我们使用应用于实际网络中的这些方法，以不降低精度的速度获得了 2.5 倍和在不到 1％精度下降的情况下获得了 4.5 倍的加速比，仍然在标准基准上实现了最先进的场景文本字符识别。