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In-Place Activated Batch Normalization

InPlace-ABN

2 papers tagged archive 2025-07-28

Introduced by Samuel Rota Bulò et al. in In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

In-Place Activated Batch Normalization, or InPlace-ABN, substitutes the conventionally used succession of BatchNorm + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for existing deep learning frameworks. It approximately halves the memory requirements during training of modern deep learning models.

PaperSourceSee Code · mapillary/inplace_abn

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.

Tasks archive 2025-07-28

11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification2
Fine-Grained Image Classification1
GPU1
General Classification1
MUlTI-LABEL-ClASSIFICATION1
Multi-Label Classification1
Object Detection1
Semantic Segmentation1
Vocal Bursts Intensity Prediction1
image-classification1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with InPlace-ABN: 2017 to 2020, peak 1 1 0 2017: 1 paper 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

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

Normalization

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