Shift invariance (Spatial invariance)
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Case study notes[1]
Introduction[edit]
Shift Invariance simply refers to the 'invariance' that a CNN has to recognising images. It allows the CNN to detect features/objects even if it does not look exactly like the images in it's training period. Shift invariance covers 'small' differences, such as movements shifts of a couple of pixels.
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How does it work or a deeper look[edit]
- Due to pooling/max pooling it is acceptable that shift invariance only covers such small changes. This is because pooling already strips the image away of it's useless features, and gives a compressed version of the input.
Examples[edit]
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External links[edit]
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