Conditional GAN

Key Insight

A plain GAN samples a random image from everything it learned, with no way to ask for a specific kind. A conditional GAN (cGAN) fixes this by feeding the class label to both the generator and the discriminator, turning generation into class conditioning — you can request "a 7" or "a cat" on demand. This project injects the label through a projection discriminator, which blends the label into the critic's score with a dot product against a learned per-class vector instead of just stapling the label on as an extra input channel — a trick that conditions more strongly for little extra cost. Training on CIFAR-10 with labels lets you verify the control works: ask for a class, and that class is what comes out.