Hi there,
I am trying to apply this SNGAN implementation on grayscale cell images, my data size is quite enough large ~ 100,000 images. I have used resnet architecture models edited by adding an extra layer to generate/discriminate 64 pxl images and gan loss. By training the model about 15K iterations (~ 10 epochs) I could not recognize visual improvement for the generated samples as they suffer from checkboard and grid artifacts.
The training curves are shown below:
I am not sure if I have to train for a longer time (more epochs), however, the training curves and the visual samples indicate abnormal case!
Any advice, please.
Hi there, I am trying to apply this SNGAN implementation on grayscale cell images, my data size is quite enough large ~ 100,000 images. I have used resnet architecture models edited by adding an extra layer to generate/discriminate 64 pxl images and gan loss. By training the model about 15K iterations (~ 10 epochs) I could not recognize visual improvement for the generated samples as they suffer from checkboard and grid artifacts. The training curves are shown below:
I am not sure if I have to train for a longer time (more epochs), however, the training curves and the visual samples indicate abnormal case! Any advice, please.