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[P] How to structure InfoGAN with the control variables conditioned on known external parameters?

I have a large set of simulation data that was generated using a Monte Carlo procedure that samples a distribution parameterized by (h, t). I want to use an InfoGAN model to provide unsupervised predictions classifications c of the simulation samples.

Now, it would be easy enough to just use the standard InfoGAN structure to do the unsupervised classification without using the external parameters (h, t), but I want the neural network to learn how c depends on (h, t). I would think that simply providing (h, t) as additional inputs to the generator and the discriminator will not provide the best results considering that for some parameters h_i and t_j that c_k is not guaranteed to exist. So, I cannot just independently sample h, t, and c when providing input for the generator. Instead, I would like InfoGAN to learn and optimize the I(c(h, t); G(z, c(h, t))) instead of I(c; G(z, c)) where c(h, t) is not known a priori.

In short, I suppose I am looking for a way to combine the unsupervised classification of InfoGAN while also providing additional conditional information as with CGAN in a way such that the relationship between the classification and the conditional information is learned.

submitted by /u/sifodeas
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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.