… by the unknown CSI, … a conditional GAN to learn the channel effects and to act as a bridge
for the gradients to pass through. By the conditional GAN, the output distribution of the channel …

… channel, justifying our proposed practical approach. We refer to Appendix B for a proof. … In
many real world scenarios the confusion matrix C is unknown. We propose RCGAN-Unknown (…

… channel multiplier which is used to control the capacity of the model. M represents the … The
original formulation of ACGAN is equivalent to always setting λ = 1, but it is unknown whether …

… ) framework to model the wireless channel based on massive raw data, and the work in [18]
employed a conditional GAN to represent unknown channels to enable the encoding and …

… Conditional GANs can be regularized with estimates of conditional moments. … This
work proposes to use GANs to perform the sampling operation. In …

… pixel values by illumination coordinates channel-wise). In this … GANbased conditional image
generation has also been … by mapping images under unknown and canonical illumination. …

… Conditional GANs provide an elegant framework for shadow … The first three dimensions are
the RGB channels of the input … the mask to form the input with 4 channels. The output of the …

… Figure 2 illustrates architectural comparison between conventional conditional GANs and
our models with the new loss terms. In conventional conditional GANs, MLE losses are applied …

… Thus, the conditional PDF of the channel output YN given the channel input SN … unknown
channel models. A dedicated neural network based on GANs is designed to learn the channel …

… unknown and the distribution GANs learn is implicit [35]. Common approaches to the discrepancy
measurement of GANs … We use p = 0.1 for 1-channel images, and p = 0.5 for 3-channel …