Deeply-Fused Nets

Understand

In this paper, we present a novel deep learning approach, deeply-fused nets.

• The central idea of our approach is deep fusion, i.e., combine the intermediate representations of base networks, where the fused output serves as the input of the remaining part of each base network, and perform such combinations deeply over several intermediate representations.
• The resulting deeply fused net enjoys several benefits.
• First, it is able to learn multi-scale representations as it enjoys the benefits of more base networks, which could form the same fused network, other than the initial group of base networks.