According to the paper, what are the two main drawbacks of uniformly increasing a network's depth and width?

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Answer

It requires a pre-trained model and prevents parallel processing.

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It makes each gradient harder to compute and increases computational cost.

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It increases the number of parameters and the spatial resolution.

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It increases the risk of overfitting and dramatically increases computational cost.

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