Contents
Why does mode collapse in GAN?
Mode collapse happens when the generator can only produce a single type of output or a small set of outputs. This may happen due to problems in training, such as the generator finds a type of data that is easily able to fool the discriminator and thus keeps generating that one type.
What is mode collapse problem?
Each iteration of generator over-optimizes for a particular discriminator, and the discriminator never manages to learn its way out of the trap. As a result the generators rotate through a small set of output types. This form of GAN failure is called mode collapse.
What happens when you misclassify a discriminator?
During discriminator training: 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.
Where does the discriminator get its data from?
Figure 1: Backpropagation in discriminator training. The discriminator’s training data comes from two sources: Real data instances, such as real pictures of people. The discriminator uses these instances as positive examples during training. Fake data instances created by the generator.
How are fake data instances used in the discriminator?
Fake data instances created by the generator. The discriminator uses these instances as negative examples during training. In Figure 1, the two “Sample” boxes represent these two data sources feeding into the discriminator. During discriminator training the generator does not train.
When does the discriminator ignore the generator loss?
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. We use the generator loss during generator training, as described in the next section.