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

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

26 Mar 2019arXiv:1903.10829archive 2025-07-28

HyunJae Lee, Hyo-Eun Kim, Hyeonseob Nam

Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective. In this paper, we aim to fully leverage the potential of styles to improve the performance of CNNs in general vision tasks. We propose a Style-based Recalibration Module (SRM), a simple yet effective architectural unit, which 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 effectively enhances the representational ability of a CNN. The proposed module is directly fed into existing CNN architectures with negligible overhead. We conduct comprehensive experiments on general image recognition as well as tasks related to styles, which verify the benefit of SRM over recent approaches such as Squeeze-and-Excitation (SE). To explain the inherent difference between SRM and SE, we provide an in-depth comparison of their representational properties.

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Tasks

Image ClassificationStyle Transfer

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification CIFAR-10 SRM-ResNet-56 Percentage correct 95.05 #142 of 265 Archive leaderboard report
Image Classification CIFAR-10 SRM-ResNet-56 Top-1 Accuracy 95.05 #142 of 265 Archive leaderboard report
Image Classification ImageNet SRM-ResNet-101 Top 1 Accuracy 78.47% #831 of 1060 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: Residual SRM, Style-based Recalibration Module

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionDense ConnectionsGlobal Average PoolingInstance NormalizationKaiming InitializationMax PoolingRandom Horizontal FlipRandom Resized CropReLUResidual BlockResidual ConnectionResidual SRMSGD with MomentumSigmoid ActivationStep DecayStyle-based Recalibration ModuleWeight Decay

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