Building a multiclass classifier that can handle classes it has never seen?
You basically want your model to say - I dont know.
For the test set created out of the original dataset with labels, plot a histogram of the probabilities of the predicted class. Are they generally higher than some threshold say 0.2 ? If so experiment using that as a threshold to flag items from your unlabeled dataset for a manual inspection. The hope is that the probabilities are low because the model is unsure.
The manual review of these flagged items will allow you to verify if this approach even works. If it works, then use that as a labeling strategy for the &quot;I dont know&quot; category and retrain along with this new label.