What is Generative Adversarial Network (GAN)?
Generative Adversarial Network, commonly known as GAN, is a machine learning framework widely used in image generation. It corresponds to a minimax two-player game. GAN consists of two neural networks, which are known as generator and discriminator. GAN belongs to unsupervised learning.
The generator receives the input data and random noise vector, generating synthetic data which will be fed into the discriminator.
The discriminator receives both input data and synthetic data, and it is trained to differentiate whether the given data is real or synthetic data.
How does it work?
During training, the training of generator and discriminator models takes place alternately. When the generator is training, the discriminator’s training stops and vice versa.
The generator model tries its best to create synthetic data that is indistinguishable from real data to fool the discriminator, while the discriminator tries its best to catch and identify the synthetic data created by the generator model.
When the discriminator successfully detects synthetic data generated by the generator, then feedback is given to the generator to improve the quality of data generated. Eventually, a point of equilibrium will reach when the discriminator obtains discrimination score of 0.5 (50%) for the input to be either fake or real.
Potential Applications in RF Industry
The most popular use case of GAN is image generation. In this case, both generator and discriminator are Convolutional Neural Networks (CNN), which is the most widely used neural network for image processing tasks.
However, there are multiple use cases of GAN in the RF industry, which we can achieve by modifying the input and output of neural networks of the generator and discriminator.
The most useful application of GAN would be data synthesis. We can generate a large dataset from a small set of RF designs, which would be useful to train AI models to perform RF design optimization.
References
Originally published at http://filpal.wordpress.com on October 22, 2023.
