<p>Sure, it is possible. This is called style transfer and there have been a lot of works on that. In a way you learn a mapping function between the manifolds of dogs to the manifolds of cats. A famous work in that direction is the CycleGAN paper (<a href="https://arxiv.org/pdf/1703.10593.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1703.10593.pdf</a>), which uses a cycle consistent loss to map from one direction to the other and back. This makes the training more stable and the resulting images closer to the initial images.</p>
A basic query about Generative adversarial Models
<p>Is it possible for the <strong>Generator</strong> to learn a distribution when <strong>noise</strong> is a specific input say <strong>n images</strong> instead of a <strong>random noise</strong>? For example, there are two categories of images with labels <strong>0</strong> and <strong>1</strong> say <strong>0 for cats</strong> and <strong>1 for dogs</strong>. Is it possible to learn the generator as we feed it a dog and it will generate a cat image against that dog image?
This query is somehow the same as deblurring images but what if no clear image is given against that blurred image but we are just given with random clear images.</p>