Automatic augmentation methods have recently become a crucial pillar for strong model performance in vision tasks. Current methods are mostly a trade-off between being simple, in-expensive or well-performing. We present a most simple automatic augmentation baseline, TrivialAugment, that outperforms previous methods almost for free. It is parameter-free and only applies a single augmentation to each image. To us, TrivialAugment's effectiveness is very unexpected. Thus, we performed very thorough experiments on its performance. First, we compare TrivialAugment to previous state-of-the-art methods in a plethora of scenarios. Then, we perform multiple ablation studies with different augmentation spaces, augmentation methods and setups to understand the crucial requirements for its performance. We condensate our learnings into recommendations to automatic augmentation users. Additionally, we provide a simple interface to use multiple automatic augmentation methods in any codebase, as well as, our full code base for reproducibility. Since our work reveals a stagnation in many parts of automatic augmentation research, we end with a short proposal of best practices for sustained future progress in automatic augmentation methods.

该研究提出了一种无需参数的基准自动增强方法TrivialAugment，只对每个图像应用单一的增强，并通过实验比较和分析其表现及其对于算法表现的重要需求，同时提出最佳实践以供参考。

TrivialAugment：无需调参却能实现最先进的数据增强