Shift invariance (Spatial invariance): Difference between revisions
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== How does it work or a deeper look == | == How does it work or a deeper look == | ||
* | * 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 == | == Examples == | ||
Revision as of 21:52, 6 April 2018
This is student work which has not yet been approved as correct by the instructor
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]
Please include some example of how your concept is actually used. Your example must include WHERE it is used, and WHAT IS BENEFIT of it being used.
Pictures, diagrams[edit]
Pictures and diagrams go a LONG way to helping someone understand a topic. Especially if your topic is a little abstract or complex. Using a picture or diagram is a two part process:
External links[edit]
- It would be helpful
- to include many links
- to other internet resources
- to help fellow students
- Please make sure the content is good
- and don't link to a google search results, please