Aug 25, 2025 · Page Summary A generative adversarial network (GAN) has two parts: The generator learns to generate plausible data. The generated instances become negative training examples for …

Aug 25, 2025 · Generative 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.

Aug 25, 2025 · Unrolled GANs: Unrolled GANs use a generator loss function that incorporates not only the current discriminator's classifications, but also the outputs of future discriminator versions.

Aug 25, 2025 · The generator loss penalizes the generator for producing a sample that the discriminator network classifies as fake. This extra chunk of network must be included in backpropagation.

Aug 25, 2025 · GANs offer an effective way to train such rich models to resemble a real distribution. To understand how they work we'll need to understand the basic structure of a GAN.

Aug 25, 2025 · GANs try to replicate a probability distribution. They should therefore use loss functions that reflect the distance between the distribution of the data generated by the GAN and the …

Aug 25, 2025 · The discriminator updates its weights through backpropagation from the discriminator loss through the discriminator network. In the next section we'll see why the generator loss connects …

Aug 25, 2025 · It's this back and forth that allows GANs to tackle otherwise intractable generative problems. We get a toehold in the difficult generative problem by starting with a much simpler …

Ein generatives kontradiktorisches Netzwerk (Generative Adversarial Network, GAN) besteht aus zwei Teilen: Der Generator lernt, plausible Daten zu generieren. Die generierten Instanzen werden zu …

Generative Adversarial Networks GANs create new data instances that resemble your training data.