Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce. However, current data augmentation techniques rely heavily on human design and domain knowledge, and existing automated approaches are yet to fully exploit the latent features in the training dataset. In this paper we propose  \textit{Parallel Adaptive GAN Data Augmentation}(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel. We demonstrate by experiments that our data augmentation strategy, with little model-specific considerations, can be easily adapted to cross-domain deep learning/machine learning tasks such as image classification and image inpainting, while significantly improving model performance in both tasks. Our source code and experimental details are available at \url{https://github.com/miaojiang1987/k-folder-data-augmentation-gan/}. Deep learning and machine learning models produce highly successful results when given sufficient training data. However, when training data is scarce, overfitting will occur and the resulting model will generalize poorly. Data augmentation(DA) ameliorates such issues by enlarging the original data set and making more effective use of the information in existing data. Much prior work has centered on data augmentation strategies based on human design, including heuristic data augmentation strategies such as crop, mirror, rotation and distortion BID15 BID21 Proceedings of the 1 st Adaptive & Multitask Learning Workshop, Long Beach, California, 2019. Copyright 2019 by the author(s). et al., 2003) , interpolating through labeled data points in feature spaces BID5 , and adversarial data augmentation strategies based on BID22 BID8 . These methods have greatly aided many deep learning tasks across several domains such as classification BID15 , image segmentation BID24 and image reconstruction/inpainting BID0 .Despite their success, these DA methods generally require domain-specific expert knowledge, manual operations and extensive amount of tuning depending on actual contexts BID3 BID6 . In particular , the need to directly operate on existing data with domain knowledge prevents many previous data augmentation strategies from being applicable to more general settings. To circumvent the need for specific domain knowledge in data augmentation, more recent work BID1 utilizes generative adversarial networks(GANs) BID10 to produce images that better encode features in the latent space of training data. By alternatively optimizing the generator G and the discriminator D in the GAN, the GAN is able to produce images similar to the original data and effectively complement the training set. It has been shown in experiments BID1 that GAN-based methods have indeed significantly boosted the performance of classifiers under limited data through automatic augmentation, but applications into other tasks are yet to be explored. Furthermore, given the computational complexity of GANs, a natural way to reduce runtime is to consider parallelism BID13 BID7 .In view of these considerations , we propose in this paper Parallel Adaptive Generative Adversarial Network Data Augmentation(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel. Our contributions can be summarized as follows:• We propose a general adaptive black-box data augmentation strategy to diversify enhance training data, with no task-specific requirements.• We also include in our model a novel K-fold parallel framework, which helps make the most use of the existing data.• Experiments over various datasets and tasks demonstrate the effectiveness of our method in different context. In sum, our paper shows that PAGANDA effectively improves the performances for different machine learning tasks with little task-specific considerations. Our strategy is not only simple to implement, but also demonstrates capability to generate onto different settings since it does not require specific information about the task being analyzed.As a further step, we are investigating the relationship between our proposed approach and other established methods. We hope to apply our idea to other generative models such as VAE BID14 and further optimize our strategy using recent theoretical advances, and wish to investigate the scenarios where the tasks involved are interrelated. Application wise, we are aiming to apply our parallel GAN model to multi-modal image synthesis/generation where training data is limited.
We present an automated adaptive data augmentation that works for multiple different tasks. 
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