Conditional GANs, or Conditional Generative Adversarial Networks, are a type of machine learning model that generates new data based on specific conditions. Unlike traditional GANs, which create data without any constraints, Conditional GANs allow users to guide the generation process by providing additional information, such as labels or images. This enables the model to produce outputs that meet particular criteria, making it useful for tasks like image synthesis and style transfer. In a Conditional GAN, two neural networks—the generator and the discriminator—compete against each other. The generator creates data samples conditioned on the input, while the discriminator evaluates whether the samples are real or fake, also considering the conditions. This adversarial training process helps improve the quality and relevance of the generated data.