Methods › Computer Vision › Pooling Operations › Shape Adaptor
Shape Adaptor
Introduced by Shikun Liu et al. in Shape Adaptor: A Learnable Resizing Module
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Shape Adaptor is a novel resizing module for neural networks. It is a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution. This module allows for a learnable shaping factor which differs from the traditional resizing layers that are fixed and deterministic.
Image Source: Liu et al.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture 21 Jun 2023 · 1 repository · arXiv:2306.11982
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Shape Adaptor: A Learnable Resizing Module 3 Aug 2020 · 1 repository · arXiv:2008.00892
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Classification | 2 |
| Neural Architecture Search | 2 |
| AutoML | 1 |
| Transfer Learning | 1 |
| image-classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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