“NoGAN: Decrappification, DeOldification, and Super Resolution”, 2019-05-03 (; backlinks; similar):
Generative models are models that generate music, images, text, and other complex data types. In recent years generative models have advanced at an astonishing rate, largely due to deep learning, and particularly due to generative adversarial models (GANs). 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.
fast.ai 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. Transfer learning refers to pre-training a model using readily available data and quick and easy to calculate loss functions, and then fine-tuning that model for a task that may have fewer labels, or be more expensive to compute. This seemed like a potential solution to the GAN training problem, so in late 2018 fast.ai worked on a transfer learning technique for generative modeling.
The pre-trained model that fast.ai 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. Then train a model to “decrappify” those images to return them to their original state. fast.ai started with a model that was pre-trained for ImageNet classification, and added a U-Net upsampling network, adding various modern tweaks to the regular U-Net. A simple fast loss function was initially used: mean squared pixel error. This U-Net could be trained in just a few minutes. Then, the loss function was replaced was a combination of other loss functions used in the generative modeling literature (more details in the f8 video) and trained for another couple of hours. The plan was then to finally add a GAN for the last few epochs—however it turned out that the results were so good that fast.ai ended up not using a GAN for the final models…
NoGAN Training: NoGAN is a new and exciting technique in GAN training that we developed, in pursuit of higher quality and more stable renders. How, and how well, it works is a bit surprising.
Here is the NoGAN training process:
Pretrain the Generator. The generator is first trained in a more conventional and easier to control manner—with Perceptual Loss (aka Feature Loss) by itself. GAN training is not introduced yet. At this point you’re training the generator as best as you can in the easiest way possible. This takes up most of the time in NoGAN training. Keep in mind: this pretraining by itself will get the generator model far. Colorization will be well-trained as a task, albeit the colors will tend toward dull tones. Self-Attention will also be well-trained at the at this stage, which is very important.
Save Generated Images From Pretrained Generator.
Pretrain the Critic as a Binary Classifier. Much like in pretraining the generator, what we aim to achieve in this step is to get as much training as possible for the critic in a more “conventional” manner which is easier to control. And there’s nothing easier than a binary classifier! Here we’re training the critic as a binary classifier of real and fake images, with the fake images being those saved in the previous step. A helpful thing to keep in mind here is that you can simply use a pre-trained critic used for another image-to-image task and refine it. This has already been done for super-resolution, where the critic’s pretrained weights were loaded from that of a critic trained for colorization. All that is needed to make use of the pre-trained critic in this case is a little fine-tuning.
Train Generator and Critic in (Almost) Normal GAN Setting. Quickly! This is the surprising part. It turns out that in this pretraining scenario, the critic will rapidly drive adjustments in the generator during GAN training. This happens during a narrow window of time before an “inflection point” of sorts is hit. After this point, there seems to be little to no benefit in training any further in this manner. In fact, if training is continued after this point, you’ll start seeing artifacts and glitches introduced in renderings.
In the case of DeOldify, training to this point requires iterating through only about 1% to 3% of ImageNet data (or roughly 2,600–7,800 iterations on a batch size of 5). This amounts to just around 30–90 minutes of GAN training, which is in stark contrast to the three to five days of progressively-sized GAN training that was done previously. Surprisingly, during that short amount of training, the change in the quality of the renderings is dramatic. In fact, this makes up the entirety of GAN training for the video model. The “artistic” and “stable” models go one step further and repeat the NoGAN training process steps 2–4 until there’s no more apparent benefit (around five repeats).
Note: a small but important change to this GAN training that deviates from conventional GANs is the use of a loss threshold that must be met by the critic before generator training commences. Until then, the critic continues training to “catch up” in order to be able to provide the generator with constructive gradients. This catch up chiefly takes place at the beginning of GAN training which immediately follows generator and critic pretraining.