Generative adversarial networks: a survey on applications and challenges
- PDF / 2,541,724 Bytes
- 24 Pages / 595.276 x 790.866 pts Page_size
- 83 Downloads / 255 Views
TRENDS AND SURVEYS
Generative adversarial networks: a survey on applications and challenges M. R. Pavan Kumar1
· Prabhu Jayagopal2
Received: 4 August 2020 / Revised: 21 September 2020 / Accepted: 2 October 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Generative model · Convolutional neural network · segmentation · Object detection · Generative adversarial network
1 Introduction Generating quality images is a challenging task in the field of computer vision and artificial intelligence, having numerous applications and research scope. Supervised machine learning and deep learning models require large and labelled datasets to generalize the decision making process. However, the availability of large and labelled databases is questioned in many domains like medical diagnosis, fault detection, intrusion detection, etc. Hence, the research community heavily depends on unsupervised learning. In unsupervised learning, the model strives to learn the structure and extracts the useful features of the data. Generative modelling is a subfield of unsupervised learning that work towards the goal of
B
Prabhu Jayagopal [email protected] M. R. Pavan Kumar [email protected]
1
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
2
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
learning the structure of the data and generate data similar to it. Generative models can be trained with high dimensional probability distributions. They can also be used in reinforcement learning, semi-supervised learning, etc. In general, generative models work on any one of these three principles: inference approximation, maximum likelihood, and Markov chains. Latent Dirichlet distribution [7], r
Data Loading...
