# Why are there transition layers in DenseNet?

The DenseNet architecture can be summarizde with this figure:

Why there are transition layers between each block?

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 network into multiple densely connected dense blocks

So, if I understood correctly, the problem is that the feature map size can change, thus we can't concatenate. But how does the addition of transition layers solve this problem?

And how can several dense blocks connected like this be more efficient that one single bigger dense block?

Furthermore, why are all standard DenseNets made of 4 dense blocks? I guess I will have the answer to this question if I understood better the previous questions.

About transition layers (convolution + pooling), I think it's just a way of downsampling the representations calculated by DenseBlocks slowly upto the end as after transition layers the representations go from $$56 \times 56$$ to $$28 \times 28$$ to $$14 \times 14$$, and so on.