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: Because deep features represent general high-level concepts, they are often "reused" for different tasks. For example, a model trained on general photos can have its deep features extracted to help classify more specific subjects, like medical images or fashion items.

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: As data passes through a network, it becomes increasingly abstract. Deep features represent the model's "understanding" of high-level semantic traits like shape, border definition, or texture. FashionLandAgency-CC-0183.jpg