  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.
