U-net with pre-trained backbones: where to make skip connections?
I'm trying to implement U-Net with PyTorch, using pre-trained networks in the encoder path.

The original U-Net paper trained a network from scratch. Are there any resources or principles on where skip connections should be placed, when a pre-trained backbone is used instead?

I already found some examples (e.g. this repo), but without any justification for the feature selection.

In the original U-Net paper features right before Max-Pool layers are used for the skip-connections.

The logic is exactly the same with pre-trained backbones: at each spatial resolution, the deepest feature layer is selected. Thanks for qubvel on github for pointing this out in an issue.
