Open-set domain adaptation is a developing and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

Abstract Generalized Category Discovery (GCD) aims to recognize both known and novel
categories from a set of unlabeled data, based on another dataset labeled with only known …

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 …

Open-set domain adaptation (OSDA) aims to achieve knowledge transfer in the presence of
both domain shift and label shift, which assumes that there exist additional unknown target …

Although machines have surpassed humans on visual recognition problems, they are still
limited to providing closed-set answers. Unlike machines, humans can cognize novel …

Generalized Category Discovery (GCD) is a crucial real-world task that aims to recognize
both known and novel categories from an unlabeled dataset by leveraging another labeled …

In the quest for unveiling novel categories at test time, we confront the inherent limitations of
traditional supervised recognition models that are restricted by a predefined category set …

Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring
knowledge from a label-rich source domain to a label-scarce target domain. However, the …

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 …

Abstract Machine learning holds tremendous promise for transforming the fundamental
practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing …