Contents

## How does GAN loss work?

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 distribution of the real data.

## What is loss in GAN?

The GAN using Wasserstein loss involves changing the notion of the discriminator into a critic that is updated more often (e.g. five times more often) than the generator model. The critic scores images with a real value instead of predicting a probability.

**What is adversarial loss?**

The adversarial loss is defined by a continuously trained discriminator network. It is a binary classifier that differentiates between ground truth data and generated data predicted by the generative network (Fig. 2).

### Is GAN an algorithm?

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 the function of a discriminator?

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.

**Why is GAN used?**

A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling. After training, the generative model can then be used to create new plausible samples on demand. GANs have very specific use cases and it can be difficult to understand these use cases when getting started.

#### 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 is a discriminator circuit?

The Foster–Seeley discriminator is a common type of FM detector circuit, invented in 1936 by Dudley E. The circuit resembles a full-wave bridge rectifier. If the input equals the carrier frequency, the two halves of the tuned transformer circuit produce the same rectified voltage and the output is zero.

**Can a gan have more than one loss function?**

One Loss Function or Two? A GAN can have two loss functions: one for generator training and one for discriminator training. How can two loss functions work together to reflect a distance measure between probability distributions?

## Which is the default loss function in TF-Gan?

Wasserstein loss: The default loss function for TF-GAN Estimators. First described in a 2017 paper. TF-GAN implements many other loss functions as well. One Loss Function or Two? A GAN can have two loss functions: one for generator training and one for discriminator training.

## Why is the minimax loss function bad for Gan?

The original GAN paper notes that the above minimax loss function can cause the GAN to get stuck in the early stages of GAN training when the discriminator’s job is very easy. The paper therefore suggests modifying the generator loss so that the generator tries to maximize log D (G (z)).

**Where does the formula for loss function come from?**

The formula derives from the cross-entropy between the real and generated distributions. The generator can’t directly affect the log (D (x)) term in the function, so, for the generator, minimizing the loss is equivalent to minimizing log (1 – D (G (z))).