ssafar
commented
9 years ago Here is an idea: add the labels (it's another image, right?) as another data source instead, and ignore the original, integer "label" field for both of your data layers (if you put a SILENCE layer on the top of them, they won't be printed all the time). You build your neural net on the top of the low-res input (the first output of the first data layer), and when you've got the restored image, you compare it with the high-res image (coming from the first output of the second data layer) using an EUCLIDEAN_LOSS. (This still doesn't solve the question of how to write out the resulting images to files on disk, for that one, you could use the Python wrapper once you have the trained net.)
llyydd007
shelhamer
I want to reimplement the great work in [1]. They use image pairs to train a deep network for image superresolution task.The problem is that the labels became a matrix rather than a number as in the classification tasks. I am not skilled at C++ programming. And I don't now how to do the modification. As far as I can see, I think I should change the data structure, to modify the data type for the label, ReadImageToDatum for data and labels twice, and it is totally messed. Could somebody give me an outline about how can I make the modification.Thanks very much ! [1]Dong C, Loy C C, He K, et al. Learning a deep convolutional network for image super-resolution[M]//Computer Vision–ECCV 2014. Springer International Publishing, 2014: 184-199.