GAN HacksPar Eric Antoine Scuccimarra
I've now been trying to train my GANs for quite a while and still haven't been too successful, but I have learned some tricks. I found this excellent article a while ago and I didn't really understand it completely at first, but after having tried a lot of its tricks I understand them now. Here are my thoughts and some additional tricks I have used:
- Item 5 from the article - use convolutional layers with strides of 2 rather than pools : one of the biggest problems in training GANs is maintaining the gradients. Since the gradients for the generator come from the discriminator vanishing or exploding gradients are a huge problem and need to be avoided at all costs. Max pools eliminate all of the gradients but one, so convolutional layers with a stride of 2 are a better way to downsample. Average pooling will also work, but I've found that stride 2 layers work better.
- With apologies to Frank Herbert, "the gradients must flow." I've had luck using dense convnets as the discriminator because of the improved gradient flow they provide.
- Item 6 - soft and noisy labels - this has helped a LOT. I haven't tried using random labels, but I have had luck using labels that are slightly off from 0 or 1, like 0.1 or 0.99. This keeps the discriminator from becoming too confident in it's predictions and the gradient to the generator exploding. I've learned that when training GANS, exploding gradients are just as bad as vanishing gradients in that the generator learns nothing.
- The article also suggests occassionally flipping the labels, which I'm not sure exactly how to interpret. In practice, if the discriminator gets too strong I will occassionally flip the labels for a few training steps to confuse it a bit and then flip them back. This seems to help the generator catch up a bit.
- One other thing I have found is that using smaller batch sizes seems to work better. When I started using the V100 GPUs I immediately increased my batch size to the max the GPU could handle, but the generator did not learn well at all. Reducing the batch size helps a lot, possibly by introducing some additional regularization to the discriminator.
- Dropout - the article mentions using dropout in the generator, which I haven't tried. I do use dropout in the discriminator, which I wasn't sure about since it will reduce the gradients, but it does help slow down the discriminator which seems to help training.
- Item 11 - I have tried to do this and wasted a lot of time. If your training has collapsed it is not likely you will be able to uncollapse it by training one network more than the other. I would suggest that rather than training one network more than the other you make sure that the networks are roughly equally matched from the start. Training the generator more, for example, tends to lead to mode collapse; training the discriminator more tends to lead to the gradients exploding or vanishing.
- Item 12 - I haven't tried this one yet but it is interesting. I've heard a lot about using auxiliary outputs to provide regularization and if I had labelled images I would definitely try this one. In fact, I may try to label my images somehow in order to do so.
- One thing that was not mentioned in the article, but which I have found very helpful, if using separate batches for real and fake images when training the discriminator. At first I thought this was a bizarre idea and wasn't sure how it would help, but it really does.
Some additional tips on how to construct a GAN:
- Start small - when I started playing with GANs I immediately made two large, deep convnets and tried to train them and they learned nothing. I recommend you start with a very small network, train it enough to make sure it is learning something, then add a layer and repeat. I still don't know what the problem with my original networks was, or if I just wasn't patient enough, but it's a lot easier to find problems if you add one layer at a time (or one block at a time) than if you start off with a 100 layer network.
- Keep things simple - training a GAN involves making sure that two networks are roughly learning at the same pace, it's a delicate dance and I would recommend not throwing too many bells and whistles into it. As in the previous tip, make sure everything is working properly first before you add some newfangled loss function or dynamic loss weighted or anything into it.