TreeNet: A lightweight One-Shot Aggregation Convolutional Network

Understand

The architecture of deep convolutional networks (CNNs) has evolved for years, becoming more accurate and faster.

• However, it is still challenging to design reasonable network structures that aim at obtaining the best accuracy under a limited computational budget.
• In this paper, we propose a Tree block, named after its appearance, which extends the One-Shot Aggregation (OSA) module while being more lightweight and flexible.
• Specifically, the Tree block replaces each of the $3\times3$ Conv layers in OSA into a stack of shallow residual block (SRB) and $1\times1$ Conv layer.