DenseNet – Towards Data Science – Medium

DenseNet architecture is new, it is a logical extension of ResNet. ResNet architecture has a fundamental building block (Identity) where you merge (additive) a previous layer into a future layer. Reasoning here is by adding additive merges we are forcing the network to learn residuals (errors i.e. diff between some previous layer and current one). In contrast, DenseNet paper proposes concatenating outputs from the previous layers instead of using the summation. Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.