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Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
To tackle the challenges of limited data, and label scarcity in the available data, we propose generating conditional synthetic data, to be used alongside real data for developing robust ML models.
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training
We also learn the joint distribution of the data samples and attributes in the source domain by employing an encoder to map attributes to the latent space via adversarial training.
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