RGAN | generative adversarial networks | Machine Learning library

RGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. RGAN has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Idea: Use generative adversarial networks (GANs) to generate real-valued time series, for medical purposes. As the title suggests. The GAN is RGAN because it uses recurrent neural networks for both encoder and decoder (specifically LSTMs). We aim to generate time series from ICU patients, using the open-access eICU dataset. However, we also generate some non-medical time-series, like sine waves and smooth functions sampled from Gaussian Processes, and MNIST digits (imagined as a time series). Sharing medical data is hard, because it comes from real people, and is naturally highly sensitive (not to mention legally protected). One workaround for this difficultly would be to create sufficiently realistic synthetic data. This synthetic data could then be used to reproducibly develop and train machine learning models, enabling better science, and ultimately better models for medicine. We claim in this paper, that synthetic data is useful when it can be used to train a model which can perform well on real data. So, we use the performance of a classifier trained on the synthetic data, then tested on real data as a measure of the quality of the data. We call this the "TSTR score". This is a way of evaluating the output of a GAN without relying on human perceptual judgements of individual samples. We also include the case where the GAN is trained in a differentially private manner, to provide stronger privacy guarantees for the training data. We mostly just use the differentially private SGD optimiser and the moments accountant from here (with some minor modifications).