cGAN
Conditional Generative Adversarial Networks (cGANs) are a class of generative models that extend GANs by conditioning both the generator and the discriminator on auxiliary information, such as class labels or attributes. The generator G takes as input a noise vector z and a conditioning variable y to produce a sample x_hat = G(z, y). The discriminator D takes a sample x and the same conditioning y and outputs a probability that x is drawn from the real data distribution given y.
The objective modifies the standard GAN loss to incorporate the conditioning. A common formulation is min_G
Conditioning can be class labels, attributes, text descriptions, or other data modalities. Training requires labeled data
Notable variants include AC-GAN, which augments the discriminator with a classifier predicting the conditioning label, promoting
Applications include image synthesis and editing, super-resolution, inpainting, and cross-domain translation. Common evaluation uses qualitative inspection