  This paper introduces a novel generative adversarial network (GAN) for
synthesizing large-scale tabular databases which contain various features such
as continuous, discrete, and binary. Technically, our GAN belongs to the
category of class-conditioned generative models with a predefined conditional
vector. However, we propose a new formulation for deriving such a vector
incorporating both binary and discrete features simultaneously. We refer to
this noble definition as compound conditional vector and employ it for training
the generator network. The core architecture of this network is a three-layered
deep residual neural network with skip connections. For improving the stability
of such complex architecture, we present a regularization scheme towards
limiting unprecedented variations on its weight vectors during training. This
regularization approach is quite compatible with the nature of adversarial
training and it is not computationally prohibitive in runtime. Furthermore, we
constantly monitor the variation of the weight vectors for identifying any
potential instabilities or irregularities to measure the strength of our
proposed regularizer. Toward this end, we also develop a new metric for
tracking sudden perturbation on the weight vectors using the singular value
decomposition theory. Finally, we evaluate the performance of our proposed
synthesis approach on six benchmarking tabular databases, namely Adult, Census,
HCDR, Cabs, News, and King. The achieved results corroborate that for the
majority of the cases, our proposed RccGAN outperforms other conventional and
modern generative models in terms of accuracy, stability, and reliability.
