[P] Is cGAN the right approach?
Hi all – Relatively new to ML, but have been doing my homework. I have an application where I am trying to generate an image based on data vectors from a non-optical domain. The mapping / relationship is unknown, but let’s assume there is some deterministic relationship. (For example, if I had ultrasonic data reflecting off a target and I wanted to generate an image of the target.) Could I use a cGAN model and train it with known reflection / image pairs? The thing I find confusing is that most if not all cGAN example use a noise vector as the input to the generator. Couldn’t I simply use my non-image (reflection) data as the input vector instead? My simplistic understanding is that the noise vector acts as a “recipe” for the unknown image, and the cGAN is learning to read the recipe through trial and error reduction.
What else should I be diving into to get this working?