BCNet: Searching for Network Width with Bilaterally Coupled Network

Understand

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints.

• To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance \wrt~different network widths.
• However, current methods mainly follow a \textit{unilaterally augmented} (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet.
• In this paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue.