Document Type
Dissertation
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
Computer Sciences
Abstract
With data protection requirements becoming stricter, the data privacy has become increasingly important and more crucial than ever. This has led to restrictions on the availability and dissemination of real-world datasets. Synthetic data offers a viable solution to overcome barriers of data access and sharing. Existing data generation methods require a great deal of user-defined rules, manual interactions and domainspecific knowledge. Moreover, they are not able to balance the trade-off between datausability and privacy. Deep learning based methods like GANs have seen remarkable success in synthesizing images by automatically learning the complicated distributions and patterns of real data. But they often suffer from instability during the training process.
DOI
https://doi.org/10.21427/js39-jq58
Recommended Citation
Singh Walia, M. (2020). Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (WCGANS-GP). A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics). doi:10.21427/js39-jq58
Publication Details
A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics) September 2020.