<p>There is <a href="https://arxiv.org/abs/1512.09300" rel="nofollow noreferrer">VAE-GAN</a>, which likely can achieve what you want, you likely don't even need the "variational" part, You might also want to look into <a href="https://arxiv.org/abs/1703.10593" rel="nofollow noreferrer">CycleGAN</a>.</p>
What are GANs that take images as input instead of latent vectors?
<p>Here I am a newcomer to the field of GAN. I know the original GANs take latent vectors as input. But if I want to complete tasks as style converting and watermark removal, the input should possibly be an image.</p>

<p>Then it leads me to think that I probably need an autoencoder to translate an image to latent vector if I want to do such work based on original GAN architectures. Is it a legit idea? </p>

<p>Now I know Pix2pix is likely what I need. But what are the early-era GAN architectures to accomplish this 'image converting' task? </p>

<p>Many thanks.</p>