  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.
