English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Although residual connection enables training very deep neural networks, itis not friendly for online inference due to its multi-branch topology. Thisencourages many researchers to work on designing DNNs without residualconnections at inference. For example, RepVGG re-parameterizes multi-branchtopology to a VGG-like (single-branch) model when deploying, showing greatperformance when the network is relatively shallow. However, RepVGG can nottransform ResNet to VGG equivalently because re-parameterizing methods can onlybe applied to linear blocks and the non-linear layers (ReLU) have to be putoutside of the residual connection which results in limited representationability, especially for deeper networks. In this paper, we aim to remedy thisproblem and propose to remove the residual connection in a vanilla ResNetequivalently by a reserving and merging (RM) operation on ResBlock.Specifically, the RM operation allows input feature maps to pass through theblock while reserving their information and merges all the information at theend of each block, which can remove residual connections without changing theoriginal output. As a plug-in method, RM Operation basically has threeadvantages: 1) its implementation makes it naturally friendly for high rationetwork pruning. 2) it helps break the depth limitation of RepVGG. 3) it leadsto better accuracy-speed trade-off network (RMNet) compared to ResNet andRepVGG. We believe the ideology of RM Operation can inspire many insights onmodel design for the community in the future. Code is available at:https://github.com/fxmeng/RMNet.

RMNet: Equivalently Removing Residual Connection from Networks