{"ID":2848900,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24044","arxiv_id":"2510.24044","title":"Mitigating Negative Transfer via Reducing Environmental Disagreement","abstract":"Unsupervised Domain Adaptation~(UDA) focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, addressing the challenge of \\emph{domain shift}. Significant domain shifts hinder effective knowledge transfer, leading to \\emph{negative transfer} and deteriorating model performance. Therefore, mitigating negative transfer is essential. This study revisits negative transfer through the lens of causally disentangled learning, emphasizing cross-domain discriminative disagreement on non-causal environmental features as a critical factor. Our theoretical analysis reveals that overreliance on non-causal environmental features as the environment evolves can cause discriminative disagreements~(termed \\emph{environmental disagreement}), thereby resulting in negative transfer. To address this, we propose Reducing Environmental Disagreement~(RED), which disentangles each sample into domain-invariant causal features and domain-specific non-causal environmental features via adversarially training domain-specific environmental feature extractors in the opposite domains. Subsequently, RED estimates and reduces environmental disagreement based on domain-specific non-causal environmental features. Experimental results confirm that RED effectively mitigates negative transfer and achieves state-of-the-art performance.","short_abstract":"Unsupervised Domain Adaptation~(UDA) focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, addressing the challenge of \\emph{domain shift}. Significant domain shifts hinder effective knowledge transfer, leading to \\emph{negative transfer} and deteriorating model performance. Ther...","url_abs":"https://arxiv.org/abs/2510.24044","url_pdf":"https://arxiv.org/pdf/2510.24044v1","authors":"[\"Hui Sun\",\"Zheng Xie\",\"Hao-Yuan He\",\"Ming Li\"]","published":"2025-10-28T03:56:20Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}