The need for large amounts of training image data with clearly defined features is a major obstacle to applying generative adversarial networks(GAN) on image generation where training data is limited but diverse, since insufficient latent feature representation in the already scarce data often leads to instability and mode collapse during GAN training. To overcome the hurdle of limited data when applying GAN to limited datasets, we propose in this paper the strategy of \textit{parallel recurrent data augmentation}, where the GAN model progressively enriches its training set with sample images constructed from GANs trained in parallel at consecutive training epochs. Experiments on a variety of small yet diverse datasets demonstrate that our method, with little model-specific considerations, produces images of better quality as compared to the images generated  without such strategy. The source code and generated images of this paper will be made public after review. Generative Adversarial Networks(GAN) BID5 ) are powerful unsupervised learning models that have recently achieved great success in learning high-dimensional distributions in various types of problems and on different datasets. In the context of image generation, the basic framework of a GAN model consists of two parts: a generator G that generates images by translating random input z into an image, and a discriminator D which determines the authenticity of a generated image x as compared to the real data. These two components are alternatively optimized against each other during the training process, with the goal of minimizing the difference between the distribution of generated image data and target distribution of real image data.A notable challenge in GAN training, however, lies in the need for large amounts of clearly labeled data to capture the diversity features across various types of images into the model. Such requirement makes it difficult or even impossible to utilize GAN in applications where the amount of available training data is small but diverse. Moreover, recent deep learning models BID6 ) have demonstrated tendencies of misrepresentation in classification tasks when influenced by adversarial noise. Such vulnerability may also translate to unsatisfactory image generation as most generative models are implemented with deep networks.Thus, given these considerations, we propose in this paper the strategy of parallel recurrent sample augmentation agnostic to specific model details. Our contributions can be summarized as follows:• We proposed a general black-box method using recurrent image addition to diversify training data and enhance its quality over a large class of GANs without model specifications.• We also includes in our model a novel K-fold parallel framework, which better augments training data by stabilizing model output and preventing overfitting.• Experiments across various datasets and GAN objectives demonstrate the effectiveness of our method using authenticity measures such as Inception Score and Frechet Inception Distance. In sum, our paper shows that parallel recurrent sample augmentation can significantly improve the quality of synthetic images for a large class of GAN models. Our strategy is not only simple to implement, but also agnostic to the specific type of GAN to be improved on.As a further step, we are investigating the relationship between our proposed approach and other established methods. One possible pathway, for instance, lies in reinforcement learning as described in BID3 that gives more control to image generation via reward designation. We also hope to apply our idea to other generative models such as the VAE BID11 ) and further optimize our strategy using recent theoretical advances.
We introduced a novel, simple, and efficient data augmentation method that boosts the performances of existing GANs when training data is limited and diverse.  
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