The paper introduces a novel approach to data augmentation using Generative Adversarial Networks (GANs) to enhance the performance of neural networks in low-data regimes. The authors design and train a Data Augmentation Generative Adversarial Network (DAGAN) that takes existing data from a source domain and learns to generate new within-class data items, thereby improving the generalization of standard classifiers and few-shot learning systems. The DAGAN is trained to generate data that is both diverse and relevant to the original data, making it effective for both vanilla classifiers and Matching Networks. Experiments on datasets such as Omniglot, EMNIST, and VGG-Face demonstrate significant improvements in accuracy, with over 13% increase in accuracy in low-data regimes and up to 1.8% improvement in Matching Networks. The primary contributions of the paper include the development of DAGAN, the demonstration of realistic data augmentation samples, and the application of DAGAN to enhance the performance of classifiers and few-shot learning systems.The paper introduces a novel approach to data augmentation using Generative Adversarial Networks (GANs) to enhance the performance of neural networks in low-data regimes. The authors design and train a Data Augmentation Generative Adversarial Network (DAGAN) that takes existing data from a source domain and learns to generate new within-class data items, thereby improving the generalization of standard classifiers and few-shot learning systems. The DAGAN is trained to generate data that is both diverse and relevant to the original data, making it effective for both vanilla classifiers and Matching Networks. Experiments on datasets such as Omniglot, EMNIST, and VGG-Face demonstrate significant improvements in accuracy, with over 13% increase in accuracy in low-data regimes and up to 1.8% improvement in Matching Networks. The primary contributions of the paper include the development of DAGAN, the demonstration of realistic data augmentation samples, and the application of DAGAN to enhance the performance of classifiers and few-shot learning systems.