  One of the issues faced in training Generative Adversarial Nets (GANs) and
their variants is the problem of mode collapse, wherein the training stability
in terms of the generative loss increases as more training data is used. In
this paper, we propose an alternative architecture via the Low-Complexity
Neural Network (LCNN), which attempts to learn models with low complexity. The
motivation is that controlling model complexity leads to models that do not
overfit the training data. We incorporate the LCNN loss function for GANs, Deep
Convolutional GANs (DCGANs) and Spectral Normalized GANs (SNGANs), in order to
develop hybrid architectures called the LCNN-GAN, LCNN-DCGAN and LCNN-SNGAN
respectively. On various large benchmark image datasets, we show that the use
of our proposed models results in stable training while avoiding the problem of
mode collapse, resulting in better training stability. We also show how the
learning behavior can be controlled by a hyperparameter in the LCNN functional,
which also provides an improved inception score.
