We present flattened convolutional neural networks that are designed for fast
feedforward execution. The redundancy of the parameters, especially weights of
the convolutional filters in convolutional neural networks has been extensively
studied and different heuristics have been proposed to construct a low rank
basis of the filters after training. In this work, we train flattened networks
that consist of consecutive sequence of one-dimensional filters across all
directions in 3D space to obtain comparable performance as conventional
convolutional networks. We tested flattened model on different datasets and
found that the flattened layer can effectively substitute for the 3D filters
without loss of accuracy. The flattened convolution pipelines provide around
two times speed-up during feedforward pass compared to the baseline model due
to the significant reduction of learning parameters. Furthermore, the proposed
method does not require efforts in manual tuning or post processing once the
model is trained.

本文提出了用于快速前馈执行的扁平卷积神经网络，该网络训练扁平化的连续序列 1D 过滤器，可通过减少学习参数以提供约两倍的速度提升，可以有效地替代 3D 过滤器而不会丧失准确性，而且无需人工调整或后处理