<p>The benefit of long skip connections (term used for those long connections) have been studied by Drozdal et al. (<a href="https://arxiv.org/pdf/1608.04117.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1608.04117.pdf</a>) and have been shown to improve gradient flow and therefore keep the values of the weight matrices stable. It is a kind of hand wavy explanation, but it is the best one we got. However, it does improve results in segmentation tasks.</p>

<p>The crop operation is used in this example, due to the author using valid convolutions, which reduce the output size and therefore the dimension does not fit anymore. In Tensorflow you would use something like <code>tf.image.resize_images</code> to get the same height width dimensions as your feature maps in the expansive part and then use <code>tf.concat</code> to concatenate the feature maps along the channel axis (typically the 4th axis in BHWC layout).</p>
U-Net Copy and Crop
<p>I have a question regarding the copy and crop arrows in the Unet architecture picture:<a href="https://i.stack.imgur.com/SnQYM.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/SnQYM.jpg" alt="enter image description here"></a></p>

<p>I understand the implementation from the contracting and the transfer to the expensive path but I dont get these intra-connections. What should copy and crop mean and what benefit brings this operation?? Is this like a skip connection in ResNet? </p>