  In Generalized Category Discovery (GCD), we cluster unlabeled samples of
known and novel classes, leveraging a training dataset of known classes. A
salient challenge arises due to domain shifts between these datasets. To
address this, we present a novel setting: Across Domain Generalized Category
Discovery (AD-GCD) and bring forth CDAD-NET (Class Discoverer Across Domains)
as a remedy. CDAD-NET is architected to synchronize potential known class
samples across both the labeled (source) and unlabeled (target) datasets, while
emphasizing the distinct categorization of the target data. To facilitate this,
we propose an entropy-driven adversarial learning strategy that accounts for
the distance distributions of target samples relative to source-domain class
prototypes. Parallelly, the discriminative nature of the shared space is upheld
through a fusion of three metric learning objectives. In the source domain, our
focus is on refining the proximity between samples and their affiliated class
prototypes, while in the target domain, we integrate a neighborhood-centric
contrastive learning mechanism, enriched with an adept neighborsmining
approach. To further accentuate the nuanced feature interrelation among
semantically aligned images, we champion the concept of conditional image
inpainting, underscoring the premise that semantically analogous images prove
more efficacious to the task than their disjointed counterparts.
Experimentally, CDAD-NET eclipses existing literature with a performance
increment of 8-15% on three AD-GCD benchmarks we present.
