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I am trying to understand how a GAN is trained. I believe understand the Adversarial training process. What I can't seem to find information on is this: do GANs use class labels in the training process? My current understanding says no - because the discriminator is simply trying to discriminate between real or fake images, while the generator is trying to create real image (but not images of any specific class.)

If this is the case, then how do researchers propose to use the discriminator network for classification tasks? the network would only be able to perform two way classification between real or fake images. The generator network would also be difficult to use, seeing as we don't know what setting of the input vector 'Z' will result in the required generated image.

angus mitchell
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It completely depends on the network you are trying to build. If you are talking specifically about the basic GAN, then you are correct. Class labels are not needed as the discriminator network is only classifying real/fake images. There is a conditional variant of the GAN (cGAN) where you do make use of the class labels in both the generator and the discriminator. This allows you to produce examples for a specific class with the generator and classify them with the discriminator (along with the real/fake classification)

From the reading that I have done, the discriminator network is just used as a tool for training the generator, and the generator is the main network of concern. Why would you use the discriminator that you used to train the GAN for classification when you could just use a ResNet or VGG net for your classification tasks. These networks would work better anyway. You are right however that using the original GAN could cause difficulty because of the mode collapse and constantly producing the same image. That is why the conditional variant was introduced.

Hope this clears things up!

Adam
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Do GANs use class labels in the training process?

The author suspected GANs doesn't require labels. This is correct. The discriminator is trained to classify real and fake images. Since we know which images are real and which are generated by the generator, we do not need labels to train the discriminator. The generator is trained to fool the discriminator, which also doesn't require labels.

This is one of the most attractive benefits of GANs [1]. Usually, we refer to methods that do not require labels as unsupervised learning. That said, if we had labels, maybe we could train a GAN that uses the labels to improve performance. This idea underlies the follow-up work by [2] who introduced the conditional GAN.

If this is the case, then how do researchers propose to use the discriminator network for classification tasks?

There seems to be a misunderstanding here. The purpose of the discriminator is NOT to act as a classifier on real data. The purpose of the discriminator is to "tell the generator how to improve its fakes". This is done by using the discriminator as a loss function, which we can backpropagate gradients through if it is a neural network. After training, we usually discard the discriminator.

The generator network would also be difficult to use, seeing as we don't know what setting of the input vector 'Z' will result in the required generated image.

It seems the underlying reason for posting the question lies here. The input vector 'Z' is chosen such that it follows some distribution, typically a normal distribution. But then what happens if we take 'Z', a random vector with normally distributed entries, and computes 'G(Z)'? We get a new vector which follows a very complicated distribution that depends on G. The entire idea of GANs is to change G such that this new complicated distribution is close to the distribution of our data. This idea is formalized with f-Divergences in [3].

[1] https://arxiv.org/abs/1406.2661

[2] https://arxiv.org/abs/1411.1784

[3] https://arxiv.org/abs/1606.00709

Alexander Mathiasen
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