通过渐进式标签筛选，解决无源域适应中的噪声累积问题。
ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation
- 基于原型一致性逐步过滤可信伪标签，剔除高噪声样本。
- 在特征空间引入双混合增强，提升难样本可分性。
- 适合处理领域偏移大、源数据不可用的迁移学习场景。
无源域适应（SFDA）旨在不使用源数据的情况下训练目标模型，其关键在于利用预训练源模型生成伪标签。然而我们发现，源模型对受严重领域偏移影响的难样本常产生高度不确定的伪标签，导致噪声在适应前即被引入，并在参数更新中不断强化。此外，这些噪声还通过特征空间传播影响邻近样本。为消除噪声累积，我们提出一种新型渐进式课程标签（ElimPCL）方法，通过原型一致性迭代筛选可信伪标签，排除高噪声样本。同时设计特征空间中的双混合增强（Dual MixUP），提升难样本的可分性，减轻噪声对邻近样本的干扰。大量实验验证了该方法的有效性，在挑战性任务上相比现有最优方法提升最高达3.4%。
原文摘要 · Abstract (English)
Source-Free Domain Adaptation (SFDA) aims to train a target model without source data, and the key is to generate pseudo-labels using a pre-trained source model. However, we observe that the source model often produces highly uncertain pseudo-labels for hard samples, particularly those heavily affected by domain shifts, leading to these noisy pseudo-labels being introduced even before adaptation and further reinforced through parameter updates. Additionally, they continuously influence neighbor samples through propagation in the feature space.To eliminate the issue of noise accumulation, we propose a novel Progressive Curriculum Labeling (ElimPCL) method, which iteratively filters trustworthy pseudo-labeled samples based on prototype consistency to exclude high-noise samples from training. Furthermore, a Dual MixUP technique is designed in the feature space to enhance the separability of hard samples, thereby mitigating the interference of noisy samples on their neighbors.Extensive experiments validate the effectiveness of ElimPCL, achieving up to a 3.4% improvement on challenging tasks compared to state-of-the-art methods.
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