they adopted where you have your original image one finis 8 by 128 and you just use strided 4x4 cons and down sample spatial resolutions and you also up keep up sampling your channel resolutions and you get a flat hidden vector of 4,000 dimensions and then you up sample using transpose convolutions back into the actual original image and you can use the reconstruction error it could be an l1 or l2 objective and I think the authors tried both both l1 and l2 and found l1 to be working slightly better as far as reconstruction goes and