| SSL benchmark on CIFAR-10, an image classification task. UDA surpases existing semi-supervised learning methods, all of which use the Wide-ResNet-28-2 architecture. UDA applies the augmentation policy found by AutoAugment using 4,000 examples. At this sample size, it matches the performance of the fully supervised setting with 50,000 examples. The same augmentation policy is applied in cases with fewer labeled examples. |
