  Open-set domain adaptation (OSDA) has gained considerable attention in many
visual recognition tasks. However, most existing OSDA approaches are limited
due to three main reasons, including: (1) the lack of essential theoretical
analysis of generalization bound, (2) the reliance on the coexistence of source
and target data during adaptation, and (3) failing to accurately estimate the
uncertainty of model predictions. We propose a Progressive Graph Learning (PGL)
framework that decomposes the target hypothesis space into the shared and
unknown subspaces, and then progressively pseudo-labels the most confident
known samples from the target domain for hypothesis adaptation. Moreover, we
tackle a more realistic source-free open-set domain adaptation (SF-OSDA)
setting that makes no assumption about the coexistence of source and target
domains, and introduce a balanced pseudo-labeling (BP-L) strategy in a
two-stage framework, namely SF-PGL. Different from PGL that applies a
class-agnostic constant threshold for all target samples for pseudo-labeling,
the SF-PGL model uniformly selects the most confident target instances from
each category at a fixed ratio. The confidence thresholds in each class are
regarded as the 'uncertainty' of learning the semantic information, which are
then used to weigh the classification loss in the adaptation step. We conducted
unsupervised and semi-supervised OSDA and SF-OSDA experiments on the benchmark
image classification and action recognition datasets. Additionally, we find
that balanced pseudo-labeling plays a significant role in improving
calibration, which makes the trained model less prone to over-confident or
under-confident predictions on the target data. Source code is available at
https://github.com/Luoyadan/SF-PGL.
