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Decrappification, DeOldification, and Super Resolution



However, GANs are notoriously difficult to train, due to requiring a large amount of data, needing many GPUs and a lot of time to train, and being highly sensitive to minor hyperparameter changes. has been working in recent years towards making a range of models easier and faster to train, with a particular focus on using transfer learning.


The pre-trained model that selected was this: Start with an image dataset and “crappify” the images, such as reducing the resolution, adding jpeg artifacts, and obscuring parts with random text.



He discovered that just a tiny bit of GAN fine-tuning on top of the process developed with could create colorized movies in just a couple of hours, at a quality beyond any automated process that had been built before.











Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other.” This same approach was adapted to the critic and U-Net based generators used in DeOldify.


Here is the NoGAN training process: In the case of DeOldify, training to this point requires iterating through only about 1% to 3% of ImageNet data (or roughly 2600 to 7800 iterations on a batch size of five).



If you took the original DeOldify model and merely colorized each frame just as you would any other image, the result was this—a flickering mess: The immediate solution that usually springs to mind is temporal modeling of some sort, whereby you enforce constraints on the model during training over a window of frames to keep colorization decisions more consistent.



The high level overview of the steps involved in creating model to produce high resolution microscopy videos from low resolution sequences is: At Salk we have been fortunate because we have produced decent results using only synthetic low resolution data.







We used L1 loss but you could also use a feature loss or some other approach to measure the difference: LossStable = loss(Y1,Y2) Our final training loss is therefore: loss = L1 + L2 + LossStable Now that we have a trained model, generating high resolution output from low resolution input is simply a matter of running the model across a sliding window of, in this case, three low resolution input images at a time.

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