Generative adversarial networks: a survey on applications and challenges

• Published in: International journal of multimedia information retrieval Vol. 10; no. 1; pp. 1 - 24
• Main Authors: Pavan Kumar, M. R., Jayagopal, Prabhu
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2021; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Autoregressive models; Computer Science; Data augmentation; Data Mining and Knowledge Discovery; Database Management; Generative adversarial networks; Image classification; Image Processing and Computer Vision; Image quality; Image reconstruction; Image segmentation; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Machine learning; Modelling; Multimedia Information Systems; Object recognition; Semantics; Trends and Surveys; segmentation; Convolutional neural network; Object detection; Generative adversarial network; Generative model
• ISSN: 2192-6611; 2192-662X
• DOI: 10.1007/s13735-020-00196-w

Deep neural networks have attained great success in handling high dimensional data, especially images. However, generating naturalistic images containing ginormous subjects for different tasks like image classification, segmentation, object detection, reconstruction, etc., is continued to be a difficult task. Generative modelling has the potential to learn any kind of data distribution in an unsupervised manner. Variational autoencoder (VAE), autoregressive models, and generative adversarial network (GAN) are the popular generative modelling approaches that generate data distributions. Among these, GANs have gained much attention from the research community in recent years in terms of generating quality images and data augmentation. In this context, we collected research articles that employed GANs for solving various tasks from popular databases and summarized them based on their application. The main objective of this article is to present the nuts and bolts of GANs, state-of-the-art related work and its applications, evaluation metrics, challenges involved in training GANs, and benchmark datasets that would benefit naive and enthusiastic researchers who are interested in working on GANs.