  Class-conditioning offers a direct means to control a Generative Adversarial
Network (GAN) based on a discrete input variable. While necessary in many
applications, the additional information provided by the class labels could
even be expected to benefit the training of the GAN itself. On the contrary, we
observe that class-conditioning causes mode collapse in limited data settings,
where unconditional learning leads to satisfactory generative ability.
Motivated by this observation, we propose a training strategy for
class-conditional GANs (cGANs) that effectively prevents the observed
mode-collapse by leveraging unconditional learning. Our training strategy
starts with an unconditional GAN and gradually injects the class conditioning
into the generator and the objective function. The proposed method for training
cGANs with limited data results not only in stable training but also in
generating high-quality images, thanks to the early-stage exploitation of the
shared information across classes. We analyze the observed mode collapse
problem in comprehensive experiments on four datasets. Our approach
demonstrates outstanding results compared with state-of-the-art methods and
established baselines. The code is available at
https://github.com/mshahbazi72/transitional-cGAN
