In the pseudo-labelling paper, it is suggested that:
For unlabeled data, Pseudo-Labels, just picking up the class which has the maximum predicted probability, are used as if they were true labels.
However, in Statefarm.ipynb, we are concatenating the predicted probabilities, and not the MAP estimates, to the trained labels.
val_pseudo = bn_model.predict(conv_val_feat, batch_size=batch_size)
comb_pseudo = np.concatenate([da_trn_labels, val_pseudo])
Aren’t we supposed to concatenate to_categorical(bn_model.predict_classes(conv_val_feat, ...) ) to the training labels here? ( or does it not matter because in this case the model was overconfident and produce just 0 and 1 probabilities anyway)