# TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation

**Authors:** Samuel G. Müller, Frank Hutter  
**arXiv:** [2103.10158](https://arxiv.org/abs/2103.10158)  
**Date:** 2021-03-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2103.10158v1/

## Key points

- The paper explores the importance of automatic augmentation methods in achieving strong performance in vision tasks
- TrivialAugment is introduced as a simple yet highly effective baseline for automatic augmentation
- TrivialAugment outperforms previous methods at almost no additional cost
- The method applies only a single augmentation to each image and its effectiveness prompted thorough experiments to evaluate its performance
- The authors identify crucial requirements for optimal performance with TrivialAugment and provide recommendations for users of automatic augmentation techniques
- A simple interface is offered for easy adoption of multiple methods and the code base is made available for reproducibility purposes
- The paper addresses stagnation observed in automatic augmentation research and proposes best practices for sustained improvement
- TrivialAugment is highlighted as an efficient baseline method with practical applications

## Summary

The paper titled "TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" by Samuel G. Müller and Frank Hutter explores the importance of automatic augmentation methods in achieving strong performance in vision tasks. The authors introduce TrivialAugment as a simple yet highly effective baseline for automatic augmentation that outperforms previous methods at almost no additional cost. This parameter-free method applies only a single augmentation to each image, and its unexpected effectiveness prompted the authors to conduct thorough experiments to evaluate its performance. Through comparisons with state-of-the-art methods and multiple ablation studies, the authors identify crucial requirements for optimal performance with TrivialAugment and provide recommendations for users of automatic augmentation techniques. They also offer a simple interface for easy adoption of multiple methods and make their code base available for reproducibility purposes. In addition to presenting their findings, the paper addresses stagnation observed in automatic augmentation research and proposes best practices for sustained improvement. Overall, this paper contributes valuable insights into the field of automatic augmentation and highlights TrivialAugment as an efficient baseline method with practical applications for users.