A New Trend Of Training GANs With Less Data: NVIDIA Joins The Gang

Following MIT, researchers at NVIDIA have recently developed a new augmented method for training Generative Adversarial Networks (GANs) with a limited amount of data. The approach is an adaptive discriminator augmentation mechanism that significantly stabilised training in limited data regimes. Machine learning models are data-hungry. As a matter of fact, in the past few years, we have seen that models that are fed with silos of data produce outstanding predictive outcomes. Alongside, with significant growth, Generative Adversarial Networks have been successfully used for various applications including high-fidelity natural image synthesis, data augmentation tasks, improving image compressions, etc. From emoting realistic expressions to traversing the deep space, and from bridging the gap between humans and machines to introduce new and unique art forms, GANs have it all covered. Although deep neural network models, including GANs, have shown impressive results, yet there remains a challenge of collecting a large number of specific datasets.