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VAE based compression model for GIFs
Welcome to my profile! See my homepage for deets..
The input data and targets may need to be transformed before feeding to the network. Add "input_transform" and "target_transform" parameters to dataset reader that are preprocessing functions for data and target respectively.
Line 88 needs to be modified to accommodate more than one frame as window size for compression
FCN networks need to be added
implement:
Transform (normalize) input data before feeding it to encoder. Add activation to output logits of final layer of decoder
Variational CAE need to be implemented based on the networks we have right now. This post describes how this should be done:
http://kvfrans.com/variational-autoencoders-explained/
logging summaries in TF at args.log_interval messes up tensorboard
L2 loss does not work well for image reconstruction/restoration. Try SSIM, MS-SSIM, and the loss proposed in https://research.nvidia.com/sites/default/files/pubs/2017-03_Loss-Functions-for/NN_ImgProc.pdf
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