How can you generate synthetic data using MATLAB s Deep Learning Toolbox for generative tasks

Question

With the help of Python, How can you generate synthetic data using MATLAB's Deep Learning Toolbox for generative tasks?

Answer 1

You can generate synthetic data using MATLAB's Deep Learning Toolbox for generative tasks by implementing a GAN (Generative Adversarial Network). Below is the code snippet showing the workflow to create artificial images:

In the above code, we are using the following:

• Generator Network:

  • Converts a random latent vector into synthetic data (e.g., images).
  • Uses transposed convolutions and tanh to scale outputs to [-1, 1].

• Discriminator Network:

  • Distinguishes between real and synthetic data.
  • Uses convolution layers and sigmoid for binary classification.

• Training:

  • trainGAN: A custom function for GAN training using adversarial loss. Define it to alternate between training the generator and discriminator.

• Synthetic Data:

  • Generate data by passing random latent vectors (Z) through the trained generator.

Hence, this example illustrates generating 28x28 grayscale images, but you can adapt it for other data types and dimensions.