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I'm currently a student doing some machine learning projects, and I want to use generative adversarial networks to train some data to discern for example, how old someone is. The intended output is a regression continuous output in term of months.
I see the DCGANs and GANs are targeting mainly classification problems. Can they be used to solve regression problems?
Thank you.
The answer is yes. There is a paper Intra-class Variation Isolation in Conditional GANs (https://arxiv.org/pdf/1811.11296.pdf) discussing it.
They propose a "C-GAN that is able to learn realistic models with continuous, semantically meaningful input parameters". They actually cover generating images of people at different ages as well.
It is possible to train a Convolutional regression model, and train it adversarially. So in theory you can. @Zhongsheng Chen linked a case of a Conditional GAN used for regression, even though I didn't find DCGANs for this, specifically.
In other words: it is possible in theory, but in practice using very large and heavy models such as GANs to run a regression is a like killing moquitoes with a bazooka.
I am also interesting about it. Recently, I found a paper which the authors used a conditional GAN for regression tasks, in hope it will drive you over this problem. For a detail about this paper, see here.
