<p>My understanding is that the authors are saying that one does not need to downsample image (or, any intermediate feature map) before applying let's say 3x3 convolution which is typical in DCNNs (e.g., VGG16 or ResNet) for feature extraction and followed by upsampling for semantic segmentation. In a typical encoder-decoder network (e.g. UNet or SegNet), one first downsamples the feature map by half, followed by convolution operation and upsampling the feature map again by 2x times.</p>
<p>All of these effects (downsampling, feature extraction and upsampling) can be captured in a single atrous convolution (of course with stride=1). Moreover, the output of an atrous convolution is a dense feature map comparing to same &quot;downsampling, feature extraction and upsampling&quot; which results in a spare feature map. See the following figure for more details. It is from <a href="https://arxiv.org/pdf/1606.00915.pdf" rel="nofollow noreferrer">DeepLabV1 paper</a>. Therefore, you can control the size of a feature map by replacing any normal convolution by atrous convolution in an intermediate layer.</p>
<p>That's also why there is a constant &quot;output_stride (input resolution / feature map resolution)&quot; of 16 in all the atrous convolutions in the picture (cascaded model) you posted above.</p>
<p><a href="https://i.stack.imgur.com/Aeqv9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Aeqv9.png" alt="enter image description here" /></a></p>
Why dilated Convolution Layer doesn't reduce the resolution of the receptive field?
<p>i try to understand dilated convolution. I already familiar with increasing the size of the kernel by filling the gaps with zeros. Its usefull to cover a bigger area and get a better understanding about larger objects.
But please can someone explain me how it is possible that dilated convolutional layers keep the origin resolution of the receptive field. It is used in the deeplabV3+ structure with a atrous rate from 2 to 16. How is it possible to use dilated convolution with a obvious bigger kernel without zero padding and the output size will be consistent.</p>

<p>deeplabV3+ structure:</p>

<p><a href="https://i.stack.imgur.com/QMs29.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/QMs29.png" alt="enter image description here"></a></p>

<p>Im confused because when i have a look at these explanation here: </p>

<p><a href="https://i.stack.imgur.com/WfD07.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WfD07.gif" alt="enter image description here"></a></p>

<p>The outputsize (3x3) of the dilated convolution layer is smaller?</p>

<p>Thank you so much for your help!</p>

<p>Lukas</p>