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Bookmark or Share- Background: What is a Generative Model? - Google DevelopersA generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.
- The Generator | Machine Learning | Google for DevelopersNote: Some GANs use non-random input to shape the output. See GAN Variations. Using the Discriminator to Train the Generator. To train a neural net, we alter the net's weights to reduce the error or loss of its output.
- Overview of GAN Structure | Machine Learning - Google DevelopersA generative adversarial network (GAN) has two parts: The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator. The discriminator learns to distinguish the generator's fake data from real data. The discriminator penalizes the generator for producing implausible results.
- GAN Training | Machine Learning | Google for DevelopersGANs must juggle two different kinds of training (generator and discriminator). GAN convergence is hard to identify. Alternating Training. The generator and the discriminator have different training processes. So how do we train the GAN as a whole? GAN training proceeds in alternating periods: The discriminator trains for one or more epochs.
- Introduction | Machine Learning | Google for DevelopersGenerative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person.
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