# Synthetic Data for Model Selection

**Authors:** Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, Matan Fintz, Gerard Medioni  
**arXiv:** [2105.00717](https://arxiv.org/abs/2105.00717)  
**Date:** 2021-05-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2105.00717v2/

## Key points

- Recent breakthroughs in synthetic data generation have led to the production of highly photorealistic images that are almost indistinguishable from real ones.
- Synthetic data offers scalability, allowing for the generation of an infinite number of images, making it a valuable tool for enhancing machine learning pipelines.
- Synthetic data can be used effectively in scenarios where authentic data is limited, such as model selection tasks in image classification.
- Researchers demonstrate that synthetic data can substitute held-out validation sets when genuine data is scarce, enabling training on a larger dataset.
- An innovative method has been introduced to calibrate error estimation from synthetic data to align with real-world domains, improving its utility for model selection purposes.

## Summary

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Recent breakthroughs in synthetic data generation approaches have revolutionized the field by enabling the production of highly photorealistic images that are nearly indistinguishable from real ones. This advancement, coupled with the scalability of synthetic generation pipelines to generate an infinite number of images, positions synthetic data as a promising tool for enhancing various machine learning (ML) pipelines. This study delves into a novel application of synthetic data for model selection, investigating its potential advantages in scenarios where authentic data is limited. By exploring image classification tasks, the researchers demonstrate that synthetic data can effectively substitute the conventional held-out validation set when genuine data is scarce, allowing for training on a larger dataset. Additionally, they introduce an innovative method to calibrate error estimation derived from synthetic data to align with that of real-world domains, significantly enhancing its utility for model selection purposes. Authored by Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, Matan Fintz, and Gerard Medioni, this research sheds light on the potential benefits of incorporating synthetic data not only for training but also for optimizing model selection processes in machine learning applications. These findings underscore the importance of considering synthetic data as a valuable resource in enhancing ML pipelines and advancing research in artificial intelligence.