Geometric Mean Pooling for Equal-Weight Multiplicative Coarse-Graining
As an alternative to the additive and extremal biases of average and max pooling, we introduce Geometric Mean Pooling (GMP), a signed pooling operator that combines the product of feature signs with the geometric mean of feature magnitudes. Motivated by local-to-global composition in quantum many-body physics, GMP retains both joint sign information and a characteristic multiplicative scale without introducing learnable pooling parameters. We show that non-overlapping hierarchical GMP preserves the corresponding global multiplicative statistic and evaluate it on synthetic sequence tasks, iterative coarse-graining, image classification, and molecular lipophilicity regression. On the synthe...
| Authors | Ang-Kun Wu, Fangdi Wen, Jingtao Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 18 September 2026 |
| Last updated at source | 25 September 2026 |
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