Which statement is true?Group of answer choicesNeural nets are not great for image data because for images, you only need to learn a few parametres but neural nets can learn many.Small changes in images don’t cause massively different image vectors.Images are not very high dimension and thus, are not very painful to train.CNNs are great for images because they are translation invariant.
Question
Which statement is true?Group of answer choicesNeural nets are not great for image data because for images, you only need to learn a few parametres but neural nets can learn many.Small changes in images don’t cause massively different image vectors.Images are not very high dimension and thus, are not very painful to train.CNNs are great for images because they are translation invariant.
Solution
The true statement is: "CNNs are great for images because they are translation invariant."
Here's why:
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Neural nets, especially Convolutional Neural Networks (CNNs), are actually excellent for image data. They can learn complex patterns and features from images, which often require many parameters.
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Small changes in images can indeed cause massively different image vectors. This is because each pixel in an image can be considered a separate feature, so even minor changes can result in a significantly different feature set.
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Images are typically high-dimensional data. Each pixel in an image contributes to its dimensionality, so a standard color image can have thousands or even millions of dimensions.
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CNNs are great for images because they are translation invariant, meaning they can recognize patterns regardless of where they are located in the image. This is a key advantage of CNNs and a primary reason why they are widely used in image recognition tasks.
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