Latent optimisation is a technique used to improve the quality of samples produced by generative adversarial networks (GANs). GANs consist of a generator and a discriminator, and the goal is to train the generator to produce samples that are indistinguishable from real data. One way to improve the quality of these samples is to use latent optimisation to refine the latent source used by the generator.
What is Latent Optimisation?
Latent optimisation is a technique used in machine learning to improve the sample quality of generative adversarial networks. In a GAN, the generator creates artificial samples, while the discriminator tries to distinguish between the real and fake samples. The goal is to train the generator to produce samples that are indistinguishable from real samples.
One way to improve the quality of these samples is to refine the latent source used by the generator. Latent optimisation does this by exploiting knowledge from the discriminator to refine the latent source. The gradient $\nabla\_{z}f\left(z\right) = \delta{f}\left(z\right)\delta{z}$ points in the direction that better satisfies the discriminator, which implies better samples.
Instead of using the randomly sampled $z \sim p\left(z\right)$, we use the optimised latent:
$$ \Delta{z} = \alpha\frac{\delta{f}\left(z\right)}{\delta{z}} $$ $$ z' = z + \Delta{z} $$
This means that the generator will use an optimised latent source to produce higher quality samples.
How Does Latent Optimisation Work?
Latent optimisation works by exploiting knowledge from the discriminator to refine the latent source used by the generator. The discriminator in a GAN tries to distinguish between real and fake samples. The generator tries to produce samples that the discriminator cannot distinguish from real data.
During training, the generator is updated to improve its ability to produce realistic samples. The latent source $z$ is used as the input to the generator to produce artificial samples. The discriminator then evaluates the generator's output to determine whether the samples are real or fake.
The gradient of the loss with respect to the latent source $\nabla\_{z}L$ is then computed using backpropagation. This gradient points in the direction that will improve the quality of the samples produced by the generator.
The optimised latent source $z'$ is then computed using the formula:
$$ z' = z + \alpha\frac{\delta{L}}{\delta{z}} $$
where $\alpha$ is the learning rate, and $\frac{\delta{L}}{\delta{z}}$ is the gradient of the loss with respect to the latent source. The optimised latent source is then used by the generator to produce the next batch of samples.
Why is Latent Optimisation Important?
Latent optimisation is important because it improves the quality of samples produced by generative adversarial networks. GANs are used in a variety of applications, including image and video generation, text-to-image synthesis, and unsupervised learning.
Generating high-quality samples is critical for the success of these applications. Latent optimisation helps to improve the quality of these samples by refining the latent source used by the generator.
Latent optimisation is a powerful technique for improving the quality of samples produced by generative adversarial networks. By exploiting knowledge from the discriminator to refine the latent source used by the generator, latent optimisation can produce higher quality samples that are indistinguishable from real data.
This technique is important for a variety of applications, including image and video generation, text-to-image synthesis, and unsupervised learning. By using latent optimisation, researchers and developers can improve the quality of their GAN models and produce more realistic and useful artificial data.
