<p><strong><em>Dense CNN is a type of Deep CNN in which each layer is connected with another layer deeper than itself.</em></strong></p>

<h2>What does that mean ?</h2>

<p>In normal CNN each layer is only connected to its siblings. Consider 4 layers,output from L1 is connected to only L2, output from L2 is connected only to L3, output from L3 is connected only to L4.</p>

<p>In a dense CNN, consider 4 layers, output from L1 is connected to L2, L3, L4, output from L2 is connected to L3, L4, output from L3 is connected to L4. </p>

<p>Here is a figure to illustrate it (source of the image is from <a href="https://arxiv.org/pdf/1608.06993.pdf" rel="nofollow noreferrer">this</a> paper):</p>

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

<h2>Why do we need to do this ?</h2>

<p>Nowadays we have neural networks with 100 layers or even more. Neural networks are trained using backpropagation. In this algorithm, gradient (derivative) of the cost function is used to update the weights of each layer. With each new layer, the value of gradient diminishes, specially if you are using sigmoid. This results in longer time to train or sometimes it doesn't train at all. This problem is also known as <em>vanishing gradient</em>. Direct connection in Dense CNN solves this problem.</p>

<p>Dense CNN are also less prone to overfitting as compared to normal CNN.</p>

<p>For more read <a href="https://arxiv.org/abs/1608.06993" rel="nofollow noreferrer">this</a> paper, it's pretty easy to follow. </p>
Difference between Deep CNN and Dense CNN
<p>I know it might be a silly question but am kind of new to machine learning and ANN.</p>

<p>Is there any difference between Deep convolutional neural network and Dense Convolutional neural network? </p>

<p>Thanks in advance!</p>