👉 Induced computing refers to the process of using machine learning models, specifically Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), to generate new data that resembles existing datasets. In this approach, a generator network creates synthetic data samples while a discriminator network evaluates their authenticity. Through iterative training, the generator learns to produce increasingly realistic data that can fool the discriminator, effectively "inducing" new data patterns that align with the training data distribution. This technique is widely used in various applications, including image synthesis, data augmentation, and enhancing privacy by generating synthetic datasets that mimic real ones without exposing sensitive information.
