  Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source
domain to a target domain that undergoes distribution shift and contains
samples from novel classes outside the source domain. Source-free OSDA
(SF-OSDA) techniques eliminate the need to access source domain samples, but
current SF-OSDA methods utilize only the known classes in the target domain for
adaptation, and require access to the entire target domain even during
inference after adaptation, to make the distinction between known and unknown
samples. In this paper, we introduce Unknown Sample Discovery (USD) as an
SF-OSDA method that utilizes a temporally ensembled teacher model to conduct
known-unknown target sample separation and adapts the student model to the
target domain over all classes using co-training and temporal consistency
between the teacher and the student. USD promotes Jensen-Shannon distance (JSD)
as an effective measure for known-unknown sample separation. Our
teacher-student framework significantly reduces error accumulation resulting
from imperfect known-unknown sample separation, while curriculum guidance helps
to reliably learn the distinction between target known and target unknown
subspaces. USD appends the target model with an unknown class node, thus
readily classifying a target sample into any of the known or unknown classes in
subsequent post-adaptation inference stages. Empirical results show that USD is
superior to existing SF-OSDA methods and is competitive with current OSDA
models that utilize both source and target domains during adaptation.
