How does a GAN work?
The steps involved in how a GAN works:
- Initialization: Two neural networks are created: a Generator (G) and a Discriminator (D).
- G is tasked with creating new data, like images or text, that closely resembles real data.
- D acts as a critic, trying to distinguish between real data (from a training dataset) and the data generated by G.
- Generator’s First Move: G takes a random noise vector as input. This noise vector contains random values and acts as the starting point for G’s creation process. Using its internal layers and learned patterns, G transforms the noise vector into a new data sample, like a generated image.
- Discriminator’s Turn: D receives two kinds of inputs:
- Real data samples from the training dataset.
- The data samples generated by G in the previous step. D’s job is to analyze each input and determine whether it’s real data or something G cooked up. It outputs a probability score between 0 and 1. A score of 1 indicates the data is likely real, and 0 suggests it’s fake.
- The Learning Process: Now, the adversarial part comes in:
- If D correctly identifies real data as real (score close to 1) and generated data as fake (score close to 0), both G and D are rewarded to a small degree. This is because they’re both doing their jobs well.
- However, the key is to continuously improve. If D consistently identifies everything correctly, it won’t learn much. So, the goal is for G to eventually trick D.
- Generator’s Improvement:
- When D mistakenly labels G’s creation as real (score close to 1), it’s a sign that G is on the right track. In this case, G receives a significant positive update, while D receives a penalty for being fooled.
- This feedback helps G improve its generation process to create more realistic data.
- Discriminator’s Adaptation:
- Conversely, if D correctly identifies G’s fake data (score close to 0), but G receives no reward, D is further strengthened in its discrimination abilities.
- This ongoing duel between G and D refines both networks over time.
As training progresses, G gets better at generating realistic data, making it harder for D to tell the difference. Ideally, G becomes so adept that D can’t reliably distinguish real from fake data. At this point, G is considered well-trained and can be used to generate new, realistic data samples.
Generative Adversarial Network (GAN)
GAN(Generative Adversarial Network) represents a cutting-edge approach to generative modeling within deep learning, often leveraging architectures like convolutional neural networks. The goal of generative modeling is to autonomously identify patterns in input data, enabling the model to produce new examples that feasibly resemble the original dataset.
This article covers everything you need to know about GAN, the Architecture of GAN, the Workings of GAN, and types of GAN Models, and so on.
Table of Content
- What is a Generative Adversarial Network?
- Types of GANs
- Architecture of GANs
- How does a GAN work?
- Implementation of a GAN
- Application Of Generative Adversarial Networks (GANs)
- Advantages of GAN
- Disadvantages of GAN
- GAN(Generative Adversarial Network)- FAQs