The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models require a lot of labelled data which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other classification challenges such as class-imbalance problem. Thus, mostly underperforming than fully supervised approaches. In our research, we propose new methods to train a deep model on image classification using limited number of labelled examples. This was achieved by extending state-of-the-art generative adversarial networks with multiple fake classes and network switchers. These new features enabled us to train a classifier using large unlabelled data while generating class-specific samples. The proposed model is label agnostic and is suitable for different classification scenarios ranging from weakly supervised to fully supervised settings. This was used to address classification challenges with limited labelled data and class-imbalance problem.