Style-based Recalibration Module
Style-based Recalibration Module
A **Style-based Recalibration Module (SRM)** is a module for convolutional neural networks that adaptively recalibrates intermediate feature maps by exploiting their styles. SRM first extracts the style information from each channel of the feature maps by style pooling, then estimates per-channel recalibration weight via channel-independent style integration. By incorporating the relative importance of individual styles into feature maps, SRM is aimed at enhancing the representational ability of a CNN.

The overall structure of SRM is illustrated in the Figure to the right. It consists of two main components: style pooling and style integration. The style pooling operator extracts style features
from each channel by summarizing feature responses across spatial dimensions. It is followed by the style integration operator, which produces example-specific style weights by utilizing the style features via channel-wise operation. The style weights finally recalibrate the feature maps to either
emphasize or suppress their information.
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
https://paperswithcode.com/paper/srm-a-style-based-recalibration-module-for
SRM : A Style-based Recalibration Module for Convolutional Neural Networks