| PreResNet164 (150)        | 78.49 ± 0.36 | 79.77 ± 0.17 | 80.18 ± 0.23     | 80.35 ± 0.16    |
| WideResNet28x10 (200)     | 80.82 ± 0.23 | 81.46 ± 0.23 | 81.91 ± 0.27     | 82.15 ± 0.27    |
Semi-Supervised Learning
In a follow-up paper SWA was applied to semi-supervised learning, where it illustrated  improvements beyond the best reported results in multiple settings. For example, with SWA you can get 95% accuracy on CIFAR-10 if you only have the training labels for 4k training data points (the previous best reported result on this problem was 93.7%). This paper also explores averaging multiple times within epochs, which can accelerate convergence and find still flatter solutions in a given time.



Figure 5. Performance of fast-SWA on semi-supervised learning with CIFAR-10. fast-SWA achieves record results in every setting considered.