Paper:
Data augmentation(DA) 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 an adaptive DA strategy based on generative models, where the training set adaptively enriches itself with sample images automatically constructed from deep generative models 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.

Review:
This paper proposes an method based on multiple GANs trained on different splits of the training data in order to generate additional samples to include to the training data. The number of added samples from each GAN depends on the quality of the generation based on their inception scores. This approach helps to improve the performance of a convolutional neural network on different tasks such as image classification, image generation and image inpainting. 
I rated this paper as weak reject because this paper is weak on several aspects
- In related work many similar approaches are missing (see below).
- In the methodology the actual formulation for the GAN is not presented.
- The main novelty of the paper seems to be the use of multiple GAN on different splits of the data, which seems a bit limited.
- The experimental evaluation is limited (see below).
Related work:
Several works with very similar intentions have been overlooked:
("A Bayesian Data Augmentation Approach for Learning Deep Models, Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, Ian Reid) uses GAN during training for generating samples for data augmentation.
("Dada:  Deep adversarial data augmentation for extremely low data regime classification", Xiaofeng Zhang,  Zhangyang Wang,  Dong Liu,  and Qing Ling) uses another GAN model for generating samples on low data regime for data augmentation
("Adversarial Learning of General Transformations for Data Augmentation", Saypraseuth Mounsaveng, David Vazquez, Ismail Ben Ayed, Marco Pedersoli), uses GAN for generating samples for data augmentation on reduced datasets.
(Triple Generative Adversarial Nets, Chongxuan Li, Kun Xu, Jun Zhu, Bo Zhang) uses a gan model for generating samples for semi-supervised learning with few labelled samples.
(Triangle Generative Adversarial NetworksZhe Gan∗, Liqun Chen∗, Weiyao Wang, Yunchen Pu, Yizhe Zhang,Hao Liu, Chunyuan Li, Lawrence Carin) in which again GAN is used to improve in semi-supervised settings.
Experimental evaluation:
In the experimental evaluation the importance of the use of k-fold is shown only on Fig.4. Additionally, in table 1 the method is compared with weak baselines (no DA, flip, crop, but not the combination of flip and crop which is standard on CIFAR10) and it is not compared with any other approach (and there are several as shown in related work).
For generating images, no quantitative values are provided, just some generation examples.