Papers › RegNet: Self-Regulated Network for Image Classification

RegNet: Self-Regulated Network for Image Classification

3 Jan 2021arXiv:2101.00590archive 2025-07-28

Jing Xu, Yu Pan, Xinglin Pan, Steven Hoi, Zhang Yi, Zenglin Xu

The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function. To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting Spatio-temporal information. We named the new regulated networks as RegNet. The regulator module can be easily implemented and appended to any ResNet architecture. We also apply the regulator module for improving the Squeeze-and-Excitation ResNet to show the generalization ability of our method. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, SE-ResNet, and other state-of-the-art architectures.

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Code

14 repositories listed; official and paper-mentioned ones first.

TrevorIkky/RegNet mentioned on GitHubpytorch report
keras-team/keras tfApache-2.0 report

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Tasks

ClassificationGeneral ClassificationImage ClassificationMedical Image Classificationimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification GasHisSDB RegNetY-3.2GF Accuracy 97.48 #7 of 8 Archive leaderboard report
Image Classification GasHisSDB RegNetY-3.2GF F1-Score 98.70 #7 of 8 Archive leaderboard report
Image Classification GasHisSDB RegNetY-3.2GF Precision 99.97 #7 of 8 Archive leaderboard report
Medical Image Classification NCT-CRC-HE-100K RegNetY-3.2GF Accuracy (%) 95.42 #3 of 7 Archive leaderboard report
Medical Image Classification NCT-CRC-HE-100K RegNetY-3.2GF F1-Score 97.39 #3 of 7 Archive leaderboard report
Medical Image Classification NCT-CRC-HE-100K RegNetY-3.2GF Precision 99.97 #3 of 7 Archive leaderboard report
Medical Image Classification NCT-CRC-HE-100K RegNetY-3.2GF Specificity 99.43 #3 of 7 Archive leaderboard report

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Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingKaiming InitializationMax PoolingReLUResidual BlockResidual Connection

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