The DenseNet architecture can be summarize with this figure :
Why there is transition layers between each blocks ?
In the papers, they justify the use of transition layers as follow :
The concatenation operation used in Eq. (2) is not viable when the size of feature-maps changes. However, an essential part of convolutional networks is pooling layers that change the size of feature-maps. To facilitate pooling in our architecture we divide the net- work into multiple densely connected dense blocks
But, if I understand what they means : the problem is that the feature map size can change, thus we can't concatenate. But how adding transition layer change this problem ?
And how can several dense blocks connected like this are more efficient that one single bigger dense block ?
Optional question : Why all standard DenseNet are made of 4 dense blocks ? I guess I will have the answer to this question if I understood better the previous questions...