Why do we sometimes need to train the discriminator and generator different amounts?
As discriminator training tries to figure out how to distinguish real data from fake, it has to learn how to recognize the generator’s flaws. That’s a different problem for a thoroughly trained generator than it is for an untrained generator that produces random output.
How do I train my discriminator in GAN?
Steps to train a GAN
- Step 1: Define the problem.
- Step 2: Define architecture of GAN.
- Step 3: Train Discriminator on real data for n epochs.
- Step 4: Generate fake inputs for generator and train discriminator on fake data.
- Step 5: Train generator with the output of discriminator.
What type of convolution are used for implementing pooling in the generator and discriminator model of Gan?
A set of transposed convolution layers are used in the generator for upsampling, while strided-convolution layers are used in the discriminator to downsample images.
What is the discriminator in a 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. The discriminator’s training data comes from two sources:
What is the difference between discriminator training and generator training?
During discriminator training the generator does not train. Its weights remain constant while it produces examples for the discriminator to train on. The discriminator connects to two loss functions. During discriminator training, the discriminator ignores the generator loss and just uses the discriminator loss.
What is the difference between discriminator loss and generator loss?
The discriminator connects to two loss functions. During discriminator training, the discriminator ignores the generator loss and just uses the discriminator loss. We use the generator loss during generator training, as described in the next section. During discriminator training:
How does a discriminator work in machine learning?
The discriminator classifies both real data and fake data from the generator. The discriminator loss penalizes the discriminator for misclassifying a real instance as fake or a fake instance as real. The discriminator updates its weights through backpropagation from the discriminator loss through the discriminator network.