rotational invariance
In an image classification problem, an algorithm's ability to successfully classify images even when the orientation of the image changes.
Plain English Explanation
In an image classification problem, an algorithm's ability to successfully classify images even when the orientation of the image changes. For example, the algorithm can still identify a tennis racket whether it is pointing up, sideways, or down. Note that rotational invariance is not always desirable; for example, an upside-down 9 shouldn't be classified as a 9. See also translational invariance and size invariance.
How is it used?
Practitioners refer to rotational invariance when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.