Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

This paper demonstrates the use of GANs to create synthetic medical data. This could be a vital method for data augmentation in domains where its availability is limited. Moreover medical data can often suffers from insufficiently anonymization, this synthetic data could be shared and published without privacy concerns, or even used to augment or enrich similar datasets collected in different or smaller cohorts of patients. The authors also present novel methods for evaluating GANs by, for instance training a supervised model on synthetic data and evaluating it on real data and vice versa. Really interesting paper, well worth the read.


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