Methods › Computer Vision › Image Model Blocks › Big-Little Module

Big-Little Module

4 papers tagged archive 2025-07-28

Introduced by Chun-Fu Chen et al. in Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition

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

Big-Little Modules are blocks for image models that have two branches: each of which represents a separate block from a deep model and a less deep counterpart. They were proposed as part of the BigLittle-Net architecture. The two branches are fused with a linear combination and unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).

Paper Source See Code · IBM/BigLittleNet

Papers archive 2025-07-28

4 shown of 4, 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.

Large Scale Neural Architecture Search with Polyharmonic Splines
20 Nov 2020 · 0 repositories · arXiv:2011.10608
WaveGrad: Estimating Gradients for Waveform Generation
2 Sep 2020 · 7 repositories · arXiv:2009.00713

Syntology ran 1 of 3 samples · 2 unverified · 1 pointer-only (licence)
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
17 Jan 2020 · 1 repository · arXiv:2001.06268

Syntology ran 1 of 11 samples · 10 unverified
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
10 Jul 2018 · 3 repositories · arXiv:1807.03848

Tasks archive 2025-07-28

15 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
image-classification	2
Fine-Grained Image Classification	1
Fine-Grained Visual Recognition	1
General Classification	1
Image Retrieval	1
Neural Architecture Search	1
Object	1
Object Recognition	1
Retrieval	1
Speech Recognition	1
Speech Synthesis	1
Text-To-Speech Synthesis	1
Transfer Learning	1
speech-recognition	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (4 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

Image Model Blocks Skip Connection Blocks

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