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Snapshot Ensembles: Train 1, get M for free
Snapshot Ensembles
Introduced by Gao Huang et al. in Snapshot Ensembles: Train 1, get M for free
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The overhead cost of training multiple deep neural networks could be very high in terms of the training time, hardware, and computational resource requirement and often acts as obstacle for creating deep ensembles. To overcome these barriers Huang et al. proposed a unique method to create ensemble which at the cost of training one model, yields multiple constituent model snapshots that can be ensembled together to create a strong learner. The core idea behind the concept is to make the model converge to several local minima along the optimization path and save the model parameters at these local minima points. During the training phase, a neural network would traverse through many such points. The lowest of all such local minima is known as the Global Minima. The larger the model, more are the number of parameters and larger the number of local minima points. This implies, there are discrete sets of weights and biases, at which the model is making fewer errors. So, every such minimum can be considered a weak but a potential learner model for the problem being solved. Multiple such snapshot of weights and biases are recorded which can later be ensembled to get a better generalized model which makes the least amount of mistakes.
Papers archive 2025-07-28
8 shown of 8, 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.
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SnapE -- Training Snapshot Ensembles of Link Prediction Models 5 Aug 2024 · 0 repositories · arXiv:2408.02707
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To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning 6 Mar 2023 · 1 repository · arXiv:2303.03374Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
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Interpretable Diversity Analysis: Visualizing Feature Representations In Low-Cost Ensembles 12 Feb 2023 · 0 repositories · arXiv:2302.05822
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Malaria Parasite Detection using Efficient Neural Ensembles 15 Oct 2021 · 1 repository
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Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective 12 May 2021 · 1 repository · arXiv:2105.05381
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MotherNets: Rapid Deep Ensemble Learning 12 Sep 2018 · 0 repositories · arXiv:1809.04270
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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs 27 Feb 2018 · 8 repositories · arXiv:1802.10026Syntology ran 3 of 14 samples · 11 unverified
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Snapshot Ensembles: Train 1, get M for free 1 Apr 2017 · 11 repositories · arXiv:1704.00109Syntology ran 1 of 1 samples · 0 unverified
Tasks archive 2025-07-28
13 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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
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