<p>1 Unpooling. </p>

<p>In the <a href="http://arxiv.org/pdf/1311.2901v3.pdf" rel="noreferrer">original paper</a> on unpooling, remaining activations are zeroed. </p>

<p>2 Deconvolution.</p>

<p>A deconvolutional layer is just the transposed of its corresponding conv layer. E.g. if conv layer's shape is <code>[height, width, previous_layer_fms, next_layer_fms]</code>, than the deconv layer will have the shape <code>[height, width, next_layer_fms, previous_layer_fms]</code>. The weights of conv and deconv layers are shared! (see <a href="http://people.idsia.ch/~ciresan/data/icann2011.pdf" rel="noreferrer">this paper</a> for instance)</p>
How does the unpooling and deconvolution work in DeConvNet
<p>I have been trying to understand how unpooling and deconvolution works in DeConvNets. </p>

<p>Unpooling</p>

<p>While during the unpooling stage, the activations are restored back to the locations of maximum activation selections, which makes sense, but what about the remaining activations? Do those remaining activations need to be restored as well or interpolated in some way or just filled as zeros in unpooled map.</p>

<p>Deconvolution</p>

<p>After the convolution section (i.e., Convolution layer, Relu, Pooling ), it is common to have more than one feature map output, which would be treated as input channels to successive layers ( Deconv.. ). How could these feature maps be combined together in order to achieve the activation map with same resolution as original input?</p>