  Automatic augmentation methods have recently become a crucial pillar for
strong model performance in vision tasks. While existing automatic augmentation
methods need to trade off simplicity, cost and performance, we present a most
simple baseline, TrivialAugment, that outperforms previous methods for almost
free. TrivialAugment is parameter-free and only applies a single augmentation
to each image. Thus, TrivialAugment's effectiveness is very unexpected to us
and we performed very thorough experiments to study its performance. First, we
compare TrivialAugment to previous state-of-the-art methods in a variety of
image classification scenarios. Then, we perform multiple ablation studies with
different augmentation spaces, augmentation methods and setups to understand
the crucial requirements for its performance. Additionally, we provide a simple
interface to facilitate the widespread adoption of automatic augmentation
methods, 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.
