Table 3.

Binary classification accuracies of simple discriminators trained to distinguish real samples from fake samples generated by each model

Dataset	Discriminators	medGAN (%)	VAE (%)	VAE-GAN (%)	WAE (%)	ARAE (%)	DAAE (%)
MIMIC-III	CNN	98.7	97.5	95.5	78.2	74.5	71.3 a
	Bi-LSTM	97.6	94.3	95.4	76.3	74.1	70.7 a
UTP	CNN	99.4	99.4	99.3	99.5	86.2	83.8 a
	Bi-LSTM	99.5	99.6	99.5	99.8	86.7	84.3

ARAE: adversarially regularized autoencoder; Bi-LSTM: bidirectional long-short-term memory; CNN: convolutional neural network; DAAE: dual adversarial autoencoder; GAN: generative adversarial network; medGAN: medical generative adversarial network; MIMIC-III: Medical Information Mart for Intensive Care-III; UTP: UT-Physicians; VAE: variational autoencoder; WAE: Wasserstein autoencoder.