Papers › Temporal Ensembling for Semi-Supervised Learning
Temporal Ensembling for Semi-Supervised Learning
Samuli Laine, Timo Aila
In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels.
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Code
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Code Syntology ran Syntology
7 samples harvested; 3 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Image Classification | CIFAR-10, 250 Labels | Ⅱ-Model | Percentage error | 53.12 | #27 of 27 | Archive leaderboard | report |
| Semi-Supervised Image Classification | CIFAR-10, 4000 Labels | Pi Model | Percentage error | 12.16 | #45 of 49 | Archive leaderboard | report |
| Semi-Supervised Image Classification | cifar-100, 10000 Labels | Temporal ensembling | Percentage error | 38.65 | #27 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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