Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add

@inproceedings{Highlander2015VeryET,
  title={Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add},
  author={Tyler Highlander and Andres Rodriguez},
  booktitle={BMVC},
  year={2015}
}
Convolutional neural networks (CNNs) are currently state-of-the-art for various classification tasks, but are computationally expensive. Propagating through the convolutional layers is very slow, as each kernel in each layer must sequentially calculate many dot products for a single forward and backward propagation which equates to $\mathcal{O}(N^{2}n^{2})$ per kernel per layer where the inputs are $N \times N$ arrays and the kernels are $n \times n$ arrays. Convolution can be efficiently… 

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