# Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data

arXiv: https://arxiv.org/abs/2109.06486 · HTML: https://mlsanity.com/paper/2109.06486/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 24 of 24)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design](https://mlsanity.com/paper/1902.00275/) (2019)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Few-shot Domain Adaptation by Causal Mechanism Transfer](https://mlsanity.com/paper/2002.03497/) (2020)
- [Domain-Adaptive Few-Shot Learning](https://mlsanity.com/paper/2003.08626/) (2020)
- [COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images](https://mlsanity.com/paper/2009.05383/) (2020)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization](https://mlsanity.com/paper/2105.08881/) (2021)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [Conditional Adversarial Generative Flow for Controllable Image Synthesis](https://mlsanity.com/paper/1904.01782/) (2019)
- [Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection](https://mlsanity.com/paper/1901.04619/) (2019)
- [A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks](https://mlsanity.com/paper/1910.02217/) (2019)
- [Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure](https://mlsanity.com/paper/1910.07899/) (2019)
- [COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach](https://mlsanity.com/paper/2102.06285/) (2021)
- [CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training](https://mlsanity.com/paper/2108.11368/) (2021)
- [Decoupling Global and Local Representations via Invertible Generative Flows](https://mlsanity.com/paper/2004.11820/) (2020)

## Built on by (most cited first, 18 of 18)

- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data](https://mlsanity.com/paper/2310.16524/) (2023)
- [Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data](https://mlsanity.com/paper/2304.03722/) (2023)
- [Synthetic data, real errors: how (not) to publish and use synthetic data](https://mlsanity.com/paper/2305.09235/) (2023)
- [Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark](https://mlsanity.com/paper/2310.16981/) (2023)
- [Position: All Current Generative Fidelity and Diversity Metrics are Flawed](https://mlsanity.com/paper/2505.22450/) (2025)
- [Improving Adversarial Robustness by Contrastive Guided Diffusion Process](https://mlsanity.com/paper/2210.09643/) (2022)
- [LaTable: Towards Large Tabular Models](https://mlsanity.com/paper/2406.17673/) (2024)
- [Struct-Bench: A Benchmark for Differentially Private Structured Text Generation](https://mlsanity.com/paper/2509.10696/) (2025)
- [Harnessing Synthetic Data from Generative AI for Statistical Inference](https://mlsanity.com/paper/2603.05396/) (2026)
- [A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets](https://mlsanity.com/paper/2402.03985/) (2024)
- [Conditional Synthetic Data Generation for Personal Thermal Comfort Models](https://mlsanity.com/paper/2203.05242/) (2022)
- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)
- [Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation](https://mlsanity.com/paper/2507.04862/) (2025)
- [Synthetic data: How could it be used for infectious disease research?](https://mlsanity.com/paper/2407.06211/) (2024)
- [Using Synthetic Data to estimate the True Error is theoretically and practically doable](https://mlsanity.com/paper/2511.00964/) (2025)
- [Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation](https://mlsanity.com/paper/2602.03791/) (2026)

## Abstract (arXiv)

Background: At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine Learning (ML) models trained with the available data, which is limited in quantity and poor in diversity, will often be biased and inaccurate. At the same time, ML algorithms designed to fight pandemics must have good performance and be developed in a time-sensitive manner. 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. Methods: We present a hybrid model consisting of a conditional generative flow and a classifier for conditional synthetic data generation. The classifier decouples the feature representation for the condition, which is fed to the flow to extract the local noise. We generate synthetic data by manipulating the local noise with fixed conditional feature representation. We also propose a semi-supervised approach to generate synthetic samples in the absence of labels for a majority of the available data. Results: We performed conditional synthetic generation for chest computed tomography (CT) scans corresponding to normal, COVID-19, and pneumonia afflicted patients. We show that our method significantly outperforms existing models both on qualitative and quantitative performance, and our semi-supervised approach can efficiently synthesize conditional samples under label scarcity. As an example of downstream use of synthetic data, we show improvement in COVID-19 detection from CT scans with conditional synthetic data augmentation.