Deep feature annotation by iterative meta-pseudo-labeling on 2D projections

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. This work proposes a methodology, namely Deep Feature Annotation (DeepFA), that dismisses the validation set and uses very few supervised samples (e.g., 1% of the dataset). DeepFA modifies the feature spaces of a DNN along with meta-pseudo-labeling iterations in a 2D non-linear projection space using the most confidently labeled samples of an optimum-path forest semi-supervised classifier. We present a comprehensive study on DeepFA and a new variant that detects the best DNN model for generalization during the pseudo-labeling iterations. We evaluate components of DeepFA on eight datasets, finding the best DeepFA approach and showing that it outperforms self-pseudo-labeling.