SIDE: Self-supervised Intermediate Domain Exploration for Source-free
  Domain Adaptation
Jiamei Liu Han Sun Yizhen Jia Jie Qin Huiyu Zhou Ningzhong Liu
Domain adaptation aims to alleviate the domain shift when transferring the
knowledge learned from the source domain to the target domain. Due to privacy
issues, source-free domain adaptation (SFDA), where source data is unavailable
during adaptation, has recently become very demanding yet challenging. Existing
SFDA methods focus on either self-supervised learning of target samples or
reconstruction of virtual source data. The former overlooks the transferable
knowledge in the source model, whilst the latter introduces even more
uncertainty. To address the above issues, this paper proposes self-supervised
intermediate domain exploration (SIDE) that effectively bridges the domain gap
with an intermediate domain, where samples are cyclically filtered out in a
self-supervised fashion. First, we propose cycle intermediate domain filtering
(CIDF) to cyclically select intermediate samples with similar distributions
over source and target domains. Second, with the aid of those intermediate
samples, an inter-domain gap transition (IDGT) module is developed to mitigate
possible distribution mismatches between the source and target data. Finally,
we introduce cross-view consistency learning (CVCL) to maintain the intrinsic
class discriminability whilst adapting the model to the target domain.
Extensive experiments on three popular benchmarks, i.e. Office-31, Office-Home
and VisDA-C, show that our proposed SIDE achieves competitive performance
against state-of-the-art methods.