GAN's for data augmentation
So there are few possible answers to your question.
First of all you are mentioning Deep Convolutional GAN's which refers to Architecture of a Network. So you can either change Architectures or change Loss functions which are used in GANs which both can have an positive or negativ effect.
Also you mention not learning the right data distribution, it also cann occur that your data is the problem, i.e. too low number of samples, not representative samples etc.

There is no definite answer to your question, you have to try which works best for your dataset.

But a few hints for losses you can try: Wassersteindistance, Improved WassersteinGAN, Boundary Equilibrium GAN. In all of their publications also the architectures are published.