Unsupervised Data Augmentation for Consistency Training
various supervised settings to inject noise and optimize the same consistency training objective on unlabeled examples. When jointly trained with labeled examples, we utilize a weighting factor to balance the supervised cross entropy and the unsupervised consistency training loss, which is illustrated in Figure 1.
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