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

26 Mar 2019 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Classification CIFAR-10 SRM-ResNet-56 Percentage correct 95.05 # 43
Image Classification ImageNet SRM-ResNet-101 Top 1 Accuracy 78.47% # 90

Methods used in the Paper