  We study an Open-World Class Discovery problem in which, given labeled
training samples from old classes, we need to discover new classes from
unlabeled test samples. There are two critical challenges to addressing this
paradigm: (a) transferring knowledge from old to new classes, and (b)
incorporating knowledge learned from new classes back to the original model. We
propose Class Discovery Kernel Network with Expansion (CD-KNet-Exp), a deep
learning framework, which utilizes the Hilbert Schmidt Independence Criterion
to bridge supervised and unsupervised information together in a systematic way,
such that the learned knowledge from old classes is distilled appropriately for
discovering new classes. Compared to competing methods, CD-KNet-Exp shows
superior performance on three publicly available benchmark datasets and a
challenging real-world radio frequency fingerprinting dataset.
