Methods › General › Regularization › Shake-Shake Regularization

Shake-Shake Regularization

5 papers tagged archive 2025-07-28

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

Shake-Shake Regularization aims to improve the generalization ability of multi-branch networks by replacing the standard summation of parallel branches with a stochastic affine combination. A typical pre-activation ResNet with 2 residual branches would follow this equation:

xᵢ₊₁ = xᵢ + ℱ(xᵢ, 𝒲ᵢ⁽¹⁾) + ℱ(xᵢ, 𝒲ᵢ⁽²⁾)

Shake-shake regularization introduces a random variable αᵢ following a uniform distribution between 0 and 1 during training:

xᵢ₊₁ = xᵢ + αℱ(xᵢ, 𝒲ᵢ⁽¹⁾) + (1-α)ℱ(xᵢ, 𝒲ᵢ⁽²⁾)

Following the same logic as for dropout, all αᵢ are set to the expected value of $0.5$ at test time.

Source: Shake-Shake regularizationSee Code · osmr/imgclsmob

Papers archive 2025-07-28

5 shown of 5, 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

8 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
Data Augmentation1
General Classification1
Image Augmentation1
Image Cropping1
Object Detection1
Retrieval1
Stochastic Optimization1

Usage over time archive 2025-07-28

Papers per year tagged with Shake-Shake Regularization: 2017 to 2019, peak 3 3 0 2017: 1 paper 2017 2018: 3 papers 2018 2019: 1 paper 2019
Papers per year the archive tags with this method, by the paper's archive date (5 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

Regularization

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