  Generative adversarial networks (GANs) have been shown to produce realistic
samples from high-dimensional distributions, but training them is considered
hard. A possible explanation for training instabilities is the inherent
imbalance between the networks: While the discriminator is trained directly on
both real and fake samples, the generator only has control over the fake
samples it produces since the real data distribution is fixed by the choice of
a given dataset. We propose a simple modification that gives the generator
control over the real samples which leads to a tempered learning process for
both generator and discriminator. The real data distribution passes through a
lens before being revealed to the discriminator, balancing the generator and
discriminator by gradually revealing more detailed features necessary to
produce high-quality results. The proposed module automatically adjusts the
learning process to the current strength of the networks, yet is generic and
easy to add to any GAN variant. In a number of experiments, we show that this
can improve quality, stability and/or convergence speed across a range of
different GAN architectures (DCGAN, LSGAN, WGAN-GP).
