CIGR: Causal Invariant Representation Learning Framework With Global Redundancy Information for Open-Set Multi-Target Domain Adaptation.

A novel source-free domain adaptation with high-confidence sample selection and feature disentanglement for machinery fault diagnosis

This paper proposes a novel SFDA with high-confidence sample selection and feature disentanglement for machinery fault diagnosis, which effectively alleviates the adverse influence of noisy pseudo-labels during the stage of adaptation.

Efficient Inference under Label Shift in Unsupervised Domain Adaptation

This paper considers a particular type of distribution shift, label shift, and develops an e-icient inference procedure for general parameters characterizing the unlabeled target population, and proposes a progressive estimation strategy that unfolds in three stages: an initial heuristic guess, a consistent estimation,...

Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation

Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and the unlabeled target domain. We introduce Joint Contrastive-Adversarial Representation Learning (JCAR), a two-stage framework that learns a common representation from the...

MDRAN: a multi-source dual-refining adaptation network for source-free domain adaptation guided by vision-language-model

This work proposes a novel criterion, termed Maximum Refinement Decision score (MRD-score), which replaces softmax with median centering to normalize source models’ predictions along both positive and negative axes, thereby harnessing both affirmative and complementary guidance.

Towards source-free domain adaptation: a model-heterogeneous perspective

A Peer-level Heterogeneous Perception Framework is proposed that departs from such paradigms by enabling balanced collaboration between heterogeneous models by introducing an auxiliary domain that is significantly different from the target domain and employ an auxiliary model with the same architecture as the source mo...

Dynamic spectral-causal risk minimization for robust cross-domain visual recognition