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@@ -103,7 +103,7 @@ This is also very straightforward – it's just one to one generator/critic iter
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#### **NoGAN**
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There's no paper here! This is a new type of GAN training that I've developed to solve some key problems in the previous DeOldify model. The gist is that you get the benefits of GAN training while spending minimal time doing direct GAN training. During this very short amount of GAN training the generator not only gets the full realistic colorization capabilities that used to take days of progressively resized GAN training, but it also doesn't accrue any of the artifacts and other ugly baggage of GANs. As far as I know this is a new technique. And it's incredibly effective.
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-The steps are as follows: First train the generator in a conventional way by itself with just the feature loss. Then you generate images from that, and train the critic on distinguishing between those outputs and real images as a basic binary classifier. Finally, you train the generator and critic together in a GAN setting (starting right at the target size of 192px in this case). This training is super fast- only 5%-40% of the Imagenet dataset is iterated through, once! More data is required for larger models.
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+The steps are as follows: First train the generator in a conventional way by itself with just the feature loss. Then you generate images from that, and train the critic on distinguishing between those outputs and real images as a basic binary classifier. Finally, you train the generator and critic together in a GAN setting (starting right at the target size of 192px in this case). You do this only until the critic loss levels out (which is usually within 1%-5% of training data), and then you repeat the cycle starting from generating generator images again, finetuning the critic on those, and GAN training in the same manner. This is repeated until there's no longer a noticeable benefit (about 4 to 7 of these cycles based on experience). You'll notice the critic loss won't do the usual "dip" at the beginning of training by this point. This approach requires very little actual GAN training- only 5%-40% of the Imagenet dataset is iterated through in total! More data is required for larger models. I attribute the limiting of GAN training to greatly reduced artifacts and errors in the end result.
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This builds upon a technique developed in collaboration with Jeremy Howard and Sylvain Gugger for Fast.AI's Lesson 7 in version 3 of Practical Deep Learning for Coders Part I. The particular lesson notebook can be found here: https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson7-superres-gan.ipynb
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Jason Antic