CONCLAD combines iterative PCA-based uncertainty scores, a small active-labeling budget, and pseudo-labeling to continuously separate and learn multiple novel classes from old classes.
A soft nearest-neighbor framework for continual semi-supervised learn- ing
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CONCLAD: COntinuous Novel CLAss Detector
CONCLAD combines iterative PCA-based uncertainty scores, a small active-labeling budget, and pseudo-labeling to continuously separate and learn multiple novel classes from old classes.