Papers › Adaptive Parametric Activation
Adaptive Parametric Activation
Konstantinos Panagiotis Alexandridis, Jiankang Deng, Anh Nguyen, Shan Luo
The activation function plays a crucial role in model optimisation, yet the optimal choice remains unclear. For example, the Sigmoid activation is the de-facto activation in balanced classification tasks, however, in imbalanced classification, it proves inappropriate due to bias towards frequent classes. In this work, we delve deeper in this phenomenon by performing a comprehensive statistical analysis in the classification and intermediate layers of both balanced and imbalanced networks and we empirically show that aligning the activation function with the data distribution, enhances the performance in both balanced and imbalanced tasks. To this end, we propose the Adaptive Parametric Activation (APA) function, a novel and versatile activation function that unifies most common activation functions under a single formula. APA can be applied in both intermediate layers and attention layers, significantly outperforming the state-of-the-art on several imbalanced benchmarks such as ImageNet-LT, iNaturalist2018, Places-LT, CIFAR100-LT and LVIS and balanced benchmarks such as ImageNet1K, COCO and V3DET. The code is available at https://github.com/kostas1515/AGLU.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | LVIS v1.0 val | SE-R101-FPN-MaskRCNN-APA | mask AP | 30.7 | #15 of 25 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | SE-R101-FPN-MaskRCNN-APA | mask APr | 23.6 | #15 of 25 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | SE-R50-FPN-MaskRCNN-APA | mask AP | 29.1 | #16 of 25 | Archive leaderboard | report |
| Instance Segmentation | LVIS v1.0 val | SE-R50-FPN-MaskRCNN-APA | mask APr | 21.6 | #16 of 25 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | APA (SE-ResNext-50) | Top-1 Accuracy | 59.1 | #17 of 69 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | APA (SE-ResNet-50) | Top-1 Accuracy | 57.9 | #25 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | APA (SE-ResNet-50) | Top-1 Accuracy | 42.0 | #11 of 29 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | APA (SE-ResNet-50) | Top-1 Accuracy | 74.8 | #16 of 43 | 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: AGLU
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