Generative Adversarial Networks (GANs) Explained
What are GANs?
Generative adversarial networks (GANs) are a type of machine learning that can create new data that looks real, like images, video, or audio. GANs work by having two neural networks compete against each other to steadily improve the results. This guide explains how GANs work and what they can potentially be used for, both positively and negatively.Understanding GANs' Potential
Like most machine learning, the quality of GAN outputs depends largely on the input data quality. GANs need diverse, accurate, and unbiased data to work well. When built properly, GANs enable new applications like:- Creating original images, music, and text automatically with little human input.
- Augmenting training datasets to improve other machine learning models.
- Editing visual media seamlessly by altering objects realistically.
- Unfortunately, also make fake user profiles that undermine identification systems.
How GANs Work
GANs have two competing neural networks:- Generator: Creates new synthetic samples trying to mimic real data.
- Discriminator: Tries to detect which samples are real and which are fake.
- The networks compete adversarially to improve the generator over time.
The GAN Training Process
1. The generator creates fake samples (counterfeit bills).2. Discriminator analyzes samples to identify reals from fakes.
3. The discriminator provides feedback to the generator on how to improve.
4. The generator adjusts to make more realistic fakes.
5. Repeat until the discriminator cannot tell the difference.