通过减少环境不一致，缓解无监督域适应中的负迁移问题。
Mitigating Negative Transfer via Reducing Environmental Disagreement
- 用对抗训练分离因果与非因果特征，识别并降低环境不一致。
- 在多个数据集上实现当前最优性能，显著提升模型泛化能力。
- 适合关注域适应中负迁移机制的算法研究者和工业应用开发者。
无监督域适应（UDA）旨在将已标注源域的知识迁移到无标注目标域，以应对域偏移挑战。严重的域偏移会阻碍有效知识迁移，导致负迁移并降低模型性能。本文从因果解耦学习视角重新审视负迁移，强调非因果环境特征上的跨域判别不一致是关键因素。理论分析表明，随着环境变化，过度依赖非因果环境特征会导致判别不一致（称为环境不一致），从而引发负迁移。为此，我们提出减少环境不一致（RED）方法，通过在相反域中对抗训练域特定环境特征提取器，将每个样本分解为域不变因果特征和域特定非因果环境特征。随后，基于域特定非因果环境特征估计并减少环境不一致。实验结果验证了RED能有效缓解负迁移，并达到当前最优性能。
原文摘要 · Abstract (English)
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.
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