You can boost inference accuracy by running predictions on multiple transformed versions of an input and averaging the results.
This paper shows that when you transform an input in different ways (like rotating an image), an AI model's errors aren't always the same. By running inference on multiple transformed versions of the same input and combining the results, you can get more accurate predictions without retraining the model. This is useful for improving accuracy or using smaller models without sacrificing performance.