Differentially Private GAN for Time Series

Generative Adversarial Networks (GANs) are a modern solution aiming to encourage public sharing of data, even if the data contains inherently private information, by generating synthetic data that looks like, but is not equal to, the data the GAN was trained on. However, GANs are prone to remembering samples from the training data, therefore...

LCT-GAN - Improving the efficiency of tabular data synthesis via latent embeddings

In the past decade data-driven approaches have been at the core of many business and research models. In critical domains such as healthcare and banking, data privacy issues are very stringent. Synthetic tabular data is an emerging solution to privacy guarantee concerns. Generative Adversarial Networks (GANs) are one of the emerging solutions...

Using autoencoders on differentially private federated learning GANs

Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical research and text prediction. However, these new fields work with ever more privacy-sensitive data. In order to maintain user privacy, a combination of federated learning,...

FCT-GAN: Fourier Neural Operator for Global Relation Enhancement in Tabular Data Synthesizing using Generative Adversarial Networks

Since the regularization of data privacy (e.g., GDPR), the effectiveness of data sharing has decreased. A promising technique to circumvent this problem is tabular data synthesis (i.e., the generation of fake tabular data that statistically resembles the original data). However, the state-of-the-art tabular data synthesis model, CTAB-GAN, fails...

UniformGAN: generative adversarial networks in uniform probability spaces: Improving correlation by leveraging integral probability transform

Sharing data is becoming increasingly difficult, due to the regulatory constraints imposed by the General Data Protection Regulation (GDPR). Businesses are not allowed to share data which contains privacy sensitive information. Synthetic data generation has emerged as a solution to this problem. State of the art generative adversarial networks ...

Comparing Quantitative Metrics for Generative Adversarial Neural Networks

Since their conception in 2014, a large number of Generative Adversarial Networks (GANs) [2] has been pro- posed and developed. GANs have achieved great results in realistic image generation, among other fields. Recently, stunning images have been produced. The theory and application of GANs has received much attention. However, the evaluation...

GAN Driven Audio Synthesis: On using adversarial training for data driven audio generation

In this study, we investigate the usage of generative adversarial networks for modelling a collection of sounds. The proposed method incites an interpretation of musical sound synthesis based on audio collections rather than synthesizer component controls. This promises the generation of arbitrarily complex sounds without the restrictions of...

Data-Efficient GAN for Synthetic Samples of Rare Classes

Camera traps are used around the world to provide data on species, population sizes and how species are interacting. However this creates a lot of work in identifying which animal was actually spotted near the camera. Attempts have been made to use deep-learning to identify animals and work correctly for animals which are not rare but the lack...

Extending CTAB-GAN+ with StackGAN architecture

As privacy regulations (e.g. European General Data Protection Regulation) often prevent valuable flows of data between stakeholders, data synthesis can play a crucial role in sharing captured value in data sets without sharing personal details. Different attempts have been made at solving this problem with Generative Adversarial Networks (GAN)...

GANAesthetic : An experience of interactively exploring aesthetically pleasing images and incorporating the human perception of beauty to discover aesthetic latent dimensions

Despite the fact that climate change is becoming increasingly dangerous and prevalent, there is still a lack of public engagement. This can be explained by the fact that the media portrays climate change as an abstract concept. The message can be more effectively communicated through visual art because it is more likely to invoke emotional...