Sage-Tips

Should loss of discriminator be increased?

The loss should be as small as possible for both the generator and the discriminator. But there is a catch: the smaller the discriminator loss becomes, the more the generator loss increases and vice versa.

How can I improve my GAN accuracy?

As part of the GAN series, this article looks into ways on how to improve GAN….In particular,

1. Change the cost function for a better optimization goal.
2. Add additional penalties to the cost function to enforce constraints.
3. Avoid overconfidence and overfitting.
4. Better ways of optimizing the model.
5. Add labels.

How are GAN model trained?

The GAN training algorithm involves training both the discriminator and the generator model in parallel. The algorithm is summarized in the figure below, taken from the original 2014 paper by Goodfellow, et al. titled “Generative Adversarial Networks.” Summary of the Generative Adversarial Network Training Algorithm.

What is discriminator in GAN?

The discriminator in a GAN is simply a classifier. It tries to distinguish real data from the data created by the generator. It could use any network architecture appropriate to the type of data it’s classifying. Figure 1: Backpropagation in discriminator training.

What does the discriminator do?

The purpose of a discriminator is to decide when the input is something you are interested in. It does this by putting out a logic pulse when the input signal meets an adjustable threshold value. All discriminators fall into one of two categories, leading edge or constant fraction.

How do you find the accuracy of a GAN model?

Twenty-four quantitative techniques for evaluating GAN generator models are listed below.

1. Average Log-likelihood.
2. Coverage Metric.
3. Inception Score (IS)
4. Modified Inception Score (m-IS)
5. Mode Score.
6. AM Score.
7. Frechet Inception Distance (FID)
8. Maximum Mean Discrepancy (MMD)

What is GAN model?

A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other to become more accurate in their predictions. Essentially, GANs create their own training data.

How to experiment with different learning rates for the discriminator and generator?

We can also experiment with different learning rates for the generator and the discriminator. For example, the following graph use the learning rate of 0.0003 for the discriminator and 0.0001 for the generator in the WGAN-GP training. Scale the image pixel value between -1 and 1. Use tanh as the output layer for the generator.

What is the best way to optimize the GAN model?

Adam optimizer usually works better than other methods. Add noise to the real and generated images before feeding them into the discriminator. The dynamics of the GAN models are not well understood yet. So some of the tips are just suggestions and the mileage may vary.

What is the effect of feature matching on GAN model?

It is good news in mitigating the mode collapse. It introduces randomness that makes the discriminator harder to overfit itself. Feature matching is effective when the GAN model is unstable during training. When mode collapses, all images created looks similar.

Is Tunning the hyperparameters of Gan worth it?

Some researchers had suggested that tunning the hyperparameters may ripe a better return than changing the cost functions. A carefully tunned learning rate may mitigate some serious GAN’s problems like mode collapse. In specific, lower the learning rate and redo the training when mode collapse happens.