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I implemented a U-Net with an additional decoder (one encoder, then it splits into two decoders). The first decoder predicts the normal segmentation label and the second decoder predicts the distance map of this label.

I want to evaluate this model against a baseline U-Net. My question is, is it fair to compare this multitask U-Net to a standard U-Net, or should I double the capacity of the decoder of the standard U-Net, as the multitask U-Net has two decoders and therefore doubled capacity in the decoder part?

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The typically accepted "fair comparison" among two models is to give them roughly the same number of parameters, so yes, doubling the size of the decoder (where size is measured in #parameters) sounds like a reasonable way to accomplish this.

Is the capacity of a multitask U-Net with two-decoders the same of a standard U-Net with doubled capacity in the decoder?

It's kind of hard to quantify model capacity, since it's not directly related to the number of parameters -- if you stack a huge number of linear layers, that doesn't increase model capacity at all compared to a single linear layer.

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