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Diary for Super Resolution Thesis

11/07/19

  • Trained SRResNet from scratch
  • Dataset used was df2k and loss was MSE
  • Loss stopped improving at around 78 epochs
  • Iterations were approximately 13k
  • Plan is to resume training but implementing a reduce learning rate on plateau schedule

12/07/19

  • Training SRResNet from checkpoint.

  • Using reduce lr on plateau schedule

  • Loss still not decreasing

  • Loss may not be a good indicator of learning progress

  • Idea is to track PSNR and watch if that increases over time

14/07/19

  • Evaluated PSNR on SRResNet model
  • Average PSNR of set5 is approximately 29.16 dB

24/07/19

  • Trained SRGAN model (with correct code)
  • Results are still poor
  • Used incorrect mean, std for the vgg network
  • Idea is to only normalize images when they go through the vgg network

25/07/19

  • Realized generated image and real image are never compared directly, only through VGG map
  • VGG expects normalized images of a certain standard
  • Reason why colours are off when generated
  • Started training job for SRResNet with normalized inputs
  • Idea is to train GAN after that

27/07/19

  • Normalized the images and trained SRResNet

  • Loss stalled at around 0.05

  • PSNR of Set5 is only 28.5 dB

  • Plan is to only train the network on DIV2K for some fine-tuning

  • Fine-tuning did not seem to be improving loss

  • Started training a GAN using the new pretrained SRResNet

28/07/19

  • SRGAN has a checkerboard artifact

  • Colour is still somewhat off

  • Implemented a new feature extractor that normalizes input image in VGGLoss

  • Added weighted MSELoss to Perceptual Loss function (to handle checkerboard artifacts)

  • Removed normalization from training code

  • Idea is to train a GAN using SRResNet.pt (instead of new normalized one)

  • Launched training job for SRGAN using updated code

29/07/19

  • Heavy checkerboard artifacts but colour mapping is still okay
  • However, only trained for 20 epochs which is roughly 4600 iterations
  • This is orders of magnitude less than the SOTA (roughly 100 000) iterations
  • Need to train for at least 20 000 epochs to see if the approach is valid
  • Confident the implementation is right

17/08/19

  • Output layer of generator has a tanh activation function
  • Still using FC network in discriminator but maybe should use FCN
  • Training job up and running
  • Created a modified SRResNet model based on the one find in xinntao repo
  • Training job should be launched after current SRGAN model

18/08/19

  • Checkerboarding gone!

  • Tanh activation is crucial

  • Network arbitrarily trains at double the speed (must have to do something with the activation function)

  • Launching job for MSRResNet

  • Average PSNR is 28.7453 dB for set5 (lower than other network)

  • Launching a job without tanh activation

  • After that, try one with bilinear interpolation instead of bicubic

  • Next step is to train a network on only the Y channel

  • In inference pipeline, crop boundary of 4 on each side as boundary effects decrease results

19/09/19

  • MSRResNet got a PSNR of 29.4 (without tanh layer)
  • Happy with that
  • Can add both SRResNet and MSRResNet in my model evaluation for thesis
  • Next step is to train a GAN with the tanh layer and without

20/09/19

  • Trained GAN without tanh layer, results are positive
  • Went to 30 epochs
  • PSNR is ~ 28.5 dB which is reasonable
  • No checkerboarding with MSRResNet
  • Might be useful to take out batch normalization in the discriminator as well
  • Once functionality to log images to /storage, launch job
  • Ready to launch long haul training job, ~ 2 to 3 days