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What's the difference between classification and segmentation in deep learning?

In particular, can the classification loss function be used for segmentation problems?

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Classification generally refers to the problem of classifying an entire entity. For example, in ImageNet, each image is given a class, regardless of whether the relevant information is only a subset of the image.

Segmentation refers to the problem of dividing up and then classifying parts of an image. For example: imagine an image of a cow. Classification would be saying this image contains a cow. Segmentation would try and divide which pixels are cow, which are grass, etc.

Both classification and segmentation are often done with a variety of loss functions. The most popular loss for classification is probably cross entropy, which may be appropriate for some forms of segmentation as well.

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  • $\begingroup$ I'd like to try GeneralizeDiceLoss optimized based on Dice_Loss, but I don't know if this loss function can be used for segmentation. In addition, I have seen the paper of GHMLoss based on FocalLoss optimization, which is used to solve the problem of classification. But I haven't done many semantic segmentation projects, so can these two loss functions be used to make semantic segmentation loss functions? $\endgroup$
    – lllittleX
    Jan 13 at 5:29

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