Deep hashing offers efficient storage and fast retrieval capabilities. As a result, it has been extensively applied to large‑scale retrieval tasks. To alleviate the dependence on high-quality annotated data, recent research has focused on unsupervised domain adaptive hashing methods, which aim to transfer knowledge from a label-rich source domain to a label-scarce target domain. However, in open-world scenarios, source domain labels are often inevitably noisy, which tends to undermine the quality of learned hash codes and induce considerable performance deterioration. To this end, we introduce a novel Robust Domain Adaptive Hashing (RDAH) method to jointly mitigate the adverse effects of label noise and domain discrepancy. Specifically, we first model the loss distribution of training samples using a two-component Gaussian mixture model to estimate each sample’s confidence, based on which the data is partitioned. Subsequently, we introduce a neighbor consistency-guided correction strategy, which leverages the semantic structure of high-confidence neighbors to perform weighted correction on noisy samples. Moreover, we design a dual-level cross-domain alignment mechanism that jointly mitigates domain shift from two complementary perspectives. Extensive experimental results validate the effectiveness and robustness of RDAH across multiple benchmark datasets.

Domain-adaptive hashing enhances discriminative hash representations by transferring knowledge from a label-rich source domain to a label-scarce target domain. It has attracted significant attention due to its ability to enable efficient cross-domain retrieval without requiring target domain labels. However, existing methods generally assume that source domain labels are completely accurate. In practice, labels obtained via web crawling or crowdsourcing often contain varying degrees of noise, which hampers semantic alignment and aggravates domain shift. To tackle these issues, we propose a novel method termed Evidential Learning and Progressive Alignment (ELPA) for domain-adaptive hashing. This method comprises two key modules: the Uncertainty-aware Noise Separation (UNS) and the Progressive Cross-domain Alignment (PCA). In the UNS, we exploit the belief and uncertainty masses obtained from the evidential learning model and utilize the posterior probabilities of a Gaussian Mixture Model to effectively distinguish clean samples from noisy ones. In PCA, we introduce a progressive partial optimal transport mechanism that prioritizes pseudo-label generation for well-aligned target samples, thereby gradually achieving class-level and global-level cross-domain alignment. Extensive experiments across multiple benchmark datasets with various noise ratios demonstrate that ELPA consistently surpasses existing state-of-the-art methods, exhibiting superior robustness and generalization capability.