# Conformalised data synthesis

arXiv: https://arxiv.org/abs/2312.08999 · HTML: https://mlsanity.com/paper/2312.08999/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 74 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 20 of 20)

- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [Pros and Cons of GAN Evaluation Measures: New Developments](https://mlsanity.com/paper/2103.09396/) (2021)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Class-Conditional Conformal Prediction with Many Classes](https://mlsanity.com/paper/2306.09335/) (2023)
- [Conformal Prediction using Conditional Histograms](https://mlsanity.com/paper/2105.08747/) (2021)
- [Synthesizing Informative Training Samples with GAN](https://mlsanity.com/paper/2204.07513/) (2022)
- [Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions](https://mlsanity.com/paper/2005.00065/) (2020)
- [A Close Look at Deep Learning with Small Data](https://mlsanity.com/paper/2003.12843/) (2020)
- [Criteria of efficiency for conformal prediction](https://mlsanity.com/paper/1603.04416/) (2016)
- [FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis](https://mlsanity.com/paper/2201.08953/) (2022)
- [FMRI data augmentation via synthesis](https://mlsanity.com/paper/1907.06134/) (2019)
- [Stable Conformal Prediction Sets](https://mlsanity.com/paper/2112.10224/) (2021)
- [Enhanced Balancing GAN: Minority-class Image Generation](https://mlsanity.com/paper/2011.00189/) (2020)
- [Sampling Strategies for GAN Synthetic Data](https://mlsanity.com/paper/1909.04689/) (2019)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [An Information-Theoretic Perspective on Overfitting and Underfitting](https://mlsanity.com/paper/2010.06076/) (2020)
- [Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose](https://mlsanity.com/paper/2102.03088/) (2021)
- [Deep Data Density Estimation through Donsker-Varadhan Representation](https://mlsanity.com/paper/2104.06612/) (2021)
- [Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection](https://mlsanity.com/paper/1910.08105/) (2019)
- [A novel Deep Learning approach for one-step Conformal Prediction approximation](https://mlsanity.com/paper/2207.12377/) (2022)

## Abstract (arXiv)

With the proliferation of increasingly complicated Deep Learning architectures, data synthesis is a highly promising technique to address the demand of data-hungry models. However, reliably assessing the quality of a 'synthesiser' model's output is an open research question with significant associated risks for high-stake domains. To address this challenge, we propose a unique synthesis algorithm that generates data from high-confidence feature space regions based on the Conformal Prediction framework. We support our proposed algorithm with a comprehensive exploration of the core parameter's influence, an in-depth discussion of practical advice, and an extensive empirical evaluation of five benchmark datasets. To show our approach's versatility on ubiquitous real-world challenges, the datasets were carefully selected for their variety of difficult characteristics: low sample count, class imbalance, and non-separability. In all trials, training sets extended with our confident synthesised data performed at least as well as the original set and frequently significantly improved Deep Learning performance by up to 61 percentage points F1-score.