A **Residual SRM** is a module for convolutional neural networks that uses a [Style-based Recalibration Module](https://paperswithcode.com/method/style-based-recalibration-module) within a [residual block](https://paperswithcode.com/method/residual-block) like structure. The Style-based Recalibration Module (SRM) adaptively recalibrates intermediate feature maps by exploiting their styles.

Branch attention can be seen as a dynamic branch selection mechanism: which to pay attention to, used with a multi-branch structure.

Branch attention

Training Very Deep Networks

Residual SRM

SRM : A Style-based Recalibration Module for Convolutional Neural Networks

A **Gated Convolution** is a type of temporal [convolution](https://paperswithcode.com/method/convolution) with a gating mechanism. Zero-padding is used to ensure that future context can not be seen.

Source	SRM : A Style-based Recalibration Module for Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com