Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …

Radio frequency fingerprint (RFF) identification is a promising technique for identifying
Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF …

Abstract Novel Class Discovery (NCD) aims to discover unknown classes without any
annotation, by exploiting the transferable knowledge already learned from a base set of …

With the recent progress in Generative Adversarial Networks (GANs), it is imperative for
media and visual forensics to develop detectors which can identify and attribute images to …

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as
an exhaustive search among all candidate beam pairs cannot be assuredly completed …

Deep learning methods have been very successful at radio frequency fingerprinting tasks,
predicting the identity of transmitting devices with high accuracy. We study radio frequency …

Novel Class Discovery (NCD) is a growing field where we are given during training a
labeled set of known classes and an unlabeled set of different classes that must be …

We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a
regularizer for learning an adversarially robust deep neural network classifier. In addition to …

Rehearsal-based approaches are a mainstay of continual learning (CL). They mitigate the
catastrophic forgetting problem by maintaining a small fixed-size buffer with a subset of data …

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-
the-art methods based on graph neural networks achieve excellent performance when all …