# Conditional GAN GANs do not have an encoder, so it is not clear how you can condition the generation on another variable. Authors propose conditioning on labels. Appending one-hot-encoded label vector to noise vector $ \min _{G} \max _{D} \mathbb{E}_{x \sim p_{\text {data}}}[\log D(x \mid y)]+\mathbb{E}_{\mathbf{z} \sim p(\mathbf{z})}[\log (1-D(G(\mathbf{z} \mid \boldsymbol{y})))] $ Discriminator also recieves the label. ![[cgan.jpg]] ## Image to image translation Conditioning GAN on other images (like edges) to generate relevant images: ![[im2im-gan.jpg]] --- ## References 1. Mirza and Osindero, Conditional Generative Adversarial Nets 2. Isola, Zhu, Zhou, Efros, Image-to-lmage Translation with Conditional Adversarial Networks