pytorch-spectral-normalization-gan
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GrayScale data
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.