What is a size invariance?
In an image classification problem, an algorithm's ability to successfully classify images even when the size of the image changes.
size invariance explained in plain English
In an image classification problem, an algorithm's ability to successfully classify images even when the size of the image changes. For example, the algorithm can still identify a cat whether it consumes 2M pixels or 200K pixels. Note that even the best image classification algorithms still have practical limits on size invariance. For example, an algorithm (or human) is unlikely to correctly classify a cat image consuming only 20 pixels. See also translational invariance and rotational invariance. See the Clustering course for more information.
Example
Practitioners refer to size invariance when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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- rotational invariance
In an image classification problem, an algorithm's ability to successfully classify images even when the orientation of the image changes.
- translational invariance
In an image classification problem, an algorithm's ability to successfully classify images even when the position of objects within the image changes.
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