Shift invariance (Spatial invariance): Difference between revisions

Revision as of 20:49, 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]

If you are discussing a THING YOU CAN TOUCH, you must explain how it works, and the parts it is made of. Google around for an "exploded technical diagram" of your thing, maybe like this example of an MRI It is likely you will reference outside links. Please attribute your work.
If you are discussing a PROCESS OR ABSTRACT CONCEPT (like fuzzy logic) you must deeply explain how it works.

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

References[edit]

↑ http://www.flaticon.com/

[1] ttp://www.flaticon.com/

[1]

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 == Introduction ==
-Please write a clear, concise description of your topic here.You will likely reference your introduction from somewhere else. Please use the following syntax at the end of each of your ideas. '''IT IS CRITICAL YOU ATTRIBUTE''' others work. Your introduction should be factual. No more than 3 or 4 sentences, please. Because you are not an expert in your topic, I expect you to triangulate your information. LOTS OF LINK TO OTHER RESOURCES PLEASE!
+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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