  Information Maximizing Generative Adversarial Network (infoGAN) can be
understood as a minimax problem involving two networks: discriminators and
generators with mutual information functions. The infoGAN incorporates various
components, including latent variables, mutual information, and objective
function. This research demonstrates that the two objective functions in
infoGAN become equivalent as the discriminator and generator sample size
approaches infinity. This equivalence is established by considering the
disparity between the empirical and population versions of the objective
function. The bound on this difference is determined by the Rademacher
complexity of the discriminator and generator function class. Furthermore, the
utilization of a two-layer network for both the discriminator and generator,
featuring Lipschitz and non-decreasing activation functions, validates this
equality
