Papers › RegNet: Self-Regulated Network for Image Classification
RegNet: Self-Regulated Network for Image Classification
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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 |
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
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