  We present a new method which leverages conditional Generative Adversarial
Networks (cGAN) to reconstruct galaxy cluster convergence from lensed CMB
temperature maps. Our model is constructed to emphasize structure and
high-frequency correctness relative to the Residual U-Net approach presented by
Caldeira, et. al. (2019). Ultimately, we demonstrate that while both models
perform similarly in the no-noise regime (as well as after random off-centering
of the cluster center), cGAN outperforms ResUNet when processing CMB maps
noised with 5uK/arcmin white noise or astrophysical foregrounds (tSZ and kSZ);
this out-performance is especially pronounced at high l, which is exactly the
regime in which the ResUNet under-performs traditional methods.
