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Shake-Shake regularization

21 May 2017arXiv:1705.07485archive 2025-07-28

Xavier Gastaldi

The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at https://github.com/xgastaldi/shake-shake

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13 repositories listed; official and paper-mentioned ones first.

xgastaldi/shake-shake officialmentioned in papermentioned on GitHubtorch report
LMaxence/Cifar10_Classification mentioned on GitHubpytorchnot reachable when probed 2026-09-18 — repositories for recent papers often appear after camera-ready report
YeongHyeon/Shake-Shake mentioned on GitHubtf report
ambujojha/SemiSupervisedLearning mentioned on GitHubpytorch report
dnddnjs/pytorch-cifar10 mentioned on GitHubpytorch report
hysts/pytorch_shake_shake mentioned on GitHubpytorch report
layumi/Cifar10-Adaboost mentioned on GitHubpytorchNOASSERTION report
loshchil/AdamW-and-SGDW mentioned on GitHubtorchBSD-3-Clause report
mariogeiger/pytorch_shake_shake mentioned on GitHubpytorch report
motokimura/shake_shake_chainer mentioned on GitHubpytorch report
osmr/imgclsmob mentioned on GitHubmxnetMIT report
owruby/shake-shake_pytorch mentioned on GitHubpytorch report
tensorflow/models mentioned on GitHubtf report

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Shake-Shake Regularization

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