<p>In the original <a href="https://arxiv.org/pdf/1505.04597.pdf" rel="nofollow noreferrer">U-Net paper</a> features right before Max-Pool layers are used for the skip-connections.</p>

<p>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 <a href="https://github.com/qubvel/segmentation_models/issues/76" rel="nofollow noreferrer">issue</a>.</p>
U-net with pre-trained backbones: where to make skip connections?
<p>I'm trying to implement U-Net with PyTorch, using pre-trained networks in the encoder path.</p>

<p>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?</p>

<p>I already found some examples (e.g. <a href="https://github.com/qubvel/segmentation_models" rel="nofollow noreferrer">this repo</a>), but without any justification for the feature selection.</p>