  Generative adversarial networks (GANs) were originally envisioned as
unsupervised generative models that learn to follow a target distribution.
Variants such as conditional GANs, auxiliary-classifier GANs (ACGANs) project
GANs on to supervised and semi-supervised learning frameworks by providing
labelled data and using multi-class discriminators. In this paper, we approach
the supervised GAN problem from a different perspective, one that is motivated
by the philosophy of the famous Persian poet Rumi who said, "The art of knowing
is knowing what to ignore." In the GAN framework, we not only provide the GAN
positive data that it must learn to model, but also present it with so-called
negative samples that it must learn to avoid - we call this "The Rumi
Framework." This formulation allows the discriminator to represent the
underlying target distribution better by learning to penalize generated samples
that are undesirable - we show that this capability accelerates the learning
process of the generator. We present a reformulation of the standard GAN (SGAN)
and least-squares GAN (LSGAN) within the Rumi setting. The advantage of the
reformulation is demonstrated by means of experiments conducted on MNIST,
Fashion MNIST, CelebA, and CIFAR-10 datasets. Finally, we consider an
application of the proposed formulation to address the important problem of
learning an under-represented class in an unbalanced dataset. The Rumi approach
results in substantially lower FID scores than the standard GAN frameworks
while possessing better generalization capability.
