Abstract
The absence of large annotated datasets to train deep neural networks (DNNs) is an issue since manual annotation is time-consuming, expensive, and error-prone. Semi-supervised learning techniques can address the problem propagating pseudo labels from supervised to unsupervised samples. However, they still require training and validation sets with many supervised samples.
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