  We present a novel self-supervised learning approach for conditional
generative adversarial networks (GANs) under a semi-supervised setting. Unlike
prior self-supervised approaches which often involve geometric augmentations on
the image space such as predicting rotation angles, our pretext task leverages
the label space. We perform augmentation by randomly sampling sensible labels
from the label space of the few labelled examples available and assigning them
as target labels to the abundant unlabelled examples from the same distribution
as that of the labelled ones. The images are then translated and grouped into
positive and negative pairs by their target labels, acting as training examples
for our pretext task which involves optimising an auxiliary match loss on the
discriminator's side. We tested our method on two challenging benchmarks,
CelebA and RaFD, and evaluated the results using standard metrics including
Fr\'{e}chet Inception Distance, Inception Score, and Attribute Classification
Rate. Extensive empirical evaluation demonstrates the effectiveness of our
proposed method over competitive baselines and existing arts. In particular,
our method surpasses the baseline with only 20% of the labelled examples used
to train the baseline.
