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Implementing "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" with FastAI framework. Apply hook (PyTorch mechanism) to calculate loss.

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Neural Style Transfer in FastAI

Implementing "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" with FastAI framework. Apply hook (PyTorch mechanism) to calculate loss.

Style Source

Stylized Samples

Reference

  1. fastai -- Extracting Intermediate Features Using Forward Hook
  2. fastai -- 06_cuda_cnn_hooks_init.ipynb, Deep Learning for Coder part 2 v3
  3. fastai -- documentation on what a callback can unpack from kwargs
  4. fastai2 -- another implementation on fast neural style transfer
  5. pytorch -- fast-neural-style
  6. cs231n -- total variation loss implementation
  7. tensorflow -- fast neural style transfer with a rich documentation
  8. arxiv -- Instance Normalization: The Missing Ingredient for Fast Stylization
  9. arxiv -- Perceptual Losses for Real-Time Style Transfer and Super-Resolution
  10. medium -- Practical Techniques for getting Style Transfer to Work

Log

[05/04/2020]

  • completed hooks callback and tensorboard callbacks

[08/04/2020] pending experiments:

  • try transformer with "downsample-first-upsample-final" design (for speeding up inference, training)
  • try total variation loss
  • try globally reducing weight for all losses
  • place test sets into validation dataloader (code refactoring)

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Implementing "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" with FastAI framework. Apply hook (PyTorch mechanism) to calculate loss.

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