Publication Type : Conference Paper
Publisher : IEEE
Source : Scopus
Url : https://doi.org/10.1109/ICOSEC51865.2021.9591692
Keywords : Data acquisition; Deep learning; Generative adversarial networks; Large dataset; Learning algorithms; Medical imaging; Network architecture; Architecture-variant GAN; Augmentation techniques; Data augmentation; Deep learning; GAN; Generative model; Large datasets; Loss- variant GAN; Over fitting problem; Training data; Computer vision
Campus : Bengaluru
School : School of Computing
Department : Computer Science and Engineering
Year : 2021
Abstract : The efficiency of deep learning algorithms will increase when it is trained on a large size of data. Over fitting problems will also be solved working on a large dataset. To collect a huge quantity of data for training a model is more challenging job. Collecting data will take more time as well as resources. The data augmentation technique will increase the diversity of data that to be trained on deep learning algorithms. This will also help in not collecting new data. This led to the need for generative models. When the set of training data is given to this generative model
Cite this Research Publication : Maria John, S. Santhanalakshmi, Image augmentation using GAN models in Computer Vision, Scopus, IEEE, 2021, https://doi.org/10.1109/ICOSEC51865.2021.9591692