Methods › General › Normalization › BatchChannel Normalization
BatchChannel Normalization
Introduced by Siyuan Qiao et al. in Rethinking Normalization and Elimination Singularity in Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Batch-Channel Normalization, or BCN, uses batch knowledge to prevent channel-normalized models from getting too close to "elimination singularities". Elimination singularities correspond to the points on the training trajectory where neurons become consistently deactivated. They cause degenerate manifolds in the loss landscape which will slow down training and harm model performances.
Papers archive 2025-07-28
1 shown of 1, 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.
-
Rethinking Normalization and Elimination Singularity in Neural Networks 21 Nov 2019 · 1 repository · arXiv:1911.09738
Tasks archive 2025-07-28
7 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 | 1 |
| Instance Segmentation | 1 |
| Object Detection | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 1 |
| image-classification | 1 |
| object-detection | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections