Methods › Computer Vision › Convolutional Neural Networks › Big-Little Net

Big-Little Net

2 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 Net is a convolutional neural network architecture for learning multi-scale feature representations. This is achieved by using a multi-branch network, which has different computational complexity at different branches with different resolutions. Through frequent merging of features from branches at distinct scales, the model obtains multi-scale features while using less computation.

It consists of Big-Little Modules, which have two branches: each of which represents a separate block from a deep model and a less deep counterpart. The two branches are fused with linear combination + 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).

PaperSourceSee Code · IBM/BigLittleNet

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

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.

TaskPapers
Image Classification1
Neural Architecture Search1
Object1
Object Recognition1
Speech Recognition1
image-classification1
speech-recognition1

Usage over time archive 2025-07-28

Papers per year tagged with Big-Little Net: 2018 to 2020, peak 1 1 0 2018: 1 paper 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

Convolutional Neural Networks

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