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gen_img
model
readme.md
srgan_model.py
srgan_tb.png
srgan_train.py
vgg19.py

readme.md

Super-Resolution Generative Adversarial Networks

Loss Function

  • For Gen loss
  1. [optional] VGG19 BottleNeck feature loss (content loss)
  2. MSE loss (content loss)
  3. Adversarial GAN loss with sigmoid
  • For Disc loss
  1. Adversarial GAN loss with sigmoid

Architecture Networks

  • Same as SRGAN paper
DIFFS SRGAN Paper ME
Weight initializer normal dist HE initializer
image scaling LR[0,1] HR[-1,1] L/HR[-1,1]
G loss content loss with vgg19 just MSE
global steps 2e5 1e5

Tensorboard

result

Elapsed time : 1d 9h 30m 52s with GTX Titan X 12GB x 1 (maxwell)

Result

VALID HR image VALID LR image
img img
Global Step 10k Global Step 25k Global Step 55k
img img img

To-Do

  • Not good performance...
  • on-editing...