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[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?

submitted by /u/miznick
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.