And as a lot of people have mentioned in here, DFT is pretty much implicated in neural networks already because of the mathematics (especially in convolutional/correlational neural networks, which often make use of the convolution theorem (which is "just" fourier coefficient multiplication) to do the convolution)
Extending this post it seems more interesting to look more generally at the correspondence with wavelet-transforms.
>especially in convolutional/correlation neural networks, which often make use of the convolution theorem to do the convolution
Is this true? With the learned filters being so much smaller than the input imagery/signals, and with "striding" operations and different boundary conditions being wrapped into these algorithms, it doesn't seem like a natural fit.
And as a lot of people have mentioned in here, DFT is pretty much implicated in neural networks already because of the mathematics (especially in convolutional/correlational neural networks, which often make use of the convolution theorem (which is "just" fourier coefficient multiplication) to do the convolution)
Extending this post it seems more interesting to look more generally at the correspondence with wavelet-transforms.