I'd like to use GANs for a 1-D image-to-image transformation

I'm working on a medical application where I have 1-D images (think ECG, PPG, etc). I'd like to use a GAN to mimic another 1-D image in a one-to-one way. One way to do transfer learning from a 2-D Cycle GAN example shown in Matlab is to replicate my 1-D image (125 X 1) into a 125 X 125 to form an image but that seems like a huge computing burden. Any ideas?

Thanks

[William Rose]

@Ram Krishnan,

125x125 is not a hge computing burden.

Goodfellow et al. (2014) use images for their examples, in the original GAN paper.

Zhu et al. (2017) use images, not 1-D signals.

Zhao et al. (2017) use images, not 1-D signals, in their paper.

I think you will need to do some original thinking. I would start with thinking about a 1-D convolution on a signal, instead of a 2-D convolution on an image, for the generator.

Good luck with your research.

[Ram Krishnan]

Thanks for your advice, William. As you say, a 1-D convolution may be necessary instead trying a transfer learning from 2-D image networks.