<p>In the original paper (Section 2), there seems to be ReLU activation function in the upsampling path?</p>
<p>&quot;Every step in the expansive path consists of an upsampling of the
feature map followed by a 2x2 convolution (“up-convolution”) that halves the
number of feature channels, a concatenation with the correspondingly cropped
feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU.&quot;</p>
<p><a href="https://arxiv.org/pdf/1505.04597.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1505.04597.pdf</a></p>
Why is there no activation function in upsampling layer of U-net?
<p>In the <a href="https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/" rel="nofollow noreferrer">U-net</a>, have activation functions in all the layers but there seems to be no activation function in the upsampling layer (that is done using transpose convolution). Why does this offer more efficiency than having an activation function?</p>

<p><a href="https://i.stack.imgur.com/vR2UR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/vR2UR.png" alt="enter image description here"></a></p>

<p>From my understanding, activation functions offer non-linearity. So, this question really is, what benefit is there to maintaining linearity in transpose convolutions but maintaining <em>non</em>-linearity on regular convolutions. Wouldn't it just always be best to have an activation function in these layers? </p>

<p>My only other intuition is that perhaps, they're trying to keep the upsampling as closely related to regular morphological interpolation methods.</p>