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2 cards from this section. Quick, good the night before a test.
What is padding in the context of image processing?
Think about how to keep the dimensions of an image consistent during processing.
Why do we use zeros for padding?
Consider how filters work and what not changing values means.
What role does padding play in convolution operations?
Think about what happens to the corners of images without padding.
True or False: Padding can help maintain the output size of an image during convolution.
Consider what padding is meant to do during operations.
A 6x6 image is convolved with a 3x3 filter. If no padding is applied, what will the size of the resulting feature map be? Would that affect accuracy in feature detection?
Calculate based on the initial dimensions and filter size.
Consider how varying amounts of padding might influence model training in machine learning. Propose different padding strategies for different image sizes.
Think about how different scenarios might require varying filters and adjustments.
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