Title: Noise-Tempered Generative Adversarial Networks. Abstract: We present a novel method to stabilize the training of generative adversarial networks. The training stability is often undermined by the limited and low-dimensional support of the probability density function of the data samples. To address this problem we propose to simultaneously train the generative adversarial networks against different additive noise models, including the noise-free case. The benefits of this approach are that: 1) The case with noise added to both real and generated samples extends the support of the probability density function of the data, while not compromising the exact matching of the original data distribution, and 2) The noise-free case allows the exact matching of the original data distribution. We demonstrate our approach with both fixed additive noise and with learned noise models. We show that our approach results in a stable and well-behaved training of even the original minimax GAN formulation. Moreover, our technique can be incorporated in most modern GAN formulations and leads to a consistent improvement on several common datasets.