用能量分数优化伪标签，提升无源域适应性能
Energy-Based Pseudo-Label Refining for Source-free Domain Adaptation
- 基于样本能量得分生成并筛选伪标签，降低噪声干扰
- 在Office-31等数据集上准确率超越现有方法
- 适合处理无源数据场景下的模型迁移任务
无源域适应（SFDA）在无法获取源数据的情况下进行模型迁移，挑战性大。现有方法依赖置信度生成伪标签，易引入噪声导致负向迁移。本文提出基于能量的伪标签精炼方法（EBPR），根据样本能量得分对所有聚类生成伪标签，并计算全局与类别级能量阈值进行选择性过滤。此外，引入对比学习策略，对困难样本进行增强对齐，以学习更具判别性的特征。在Office-31、Office-Home和VisDA-C数据集上的实验表明，该方法持续优于当前最先进方法。
原文摘要 · Abstract (English)
Source-free domain adaptation (SFDA), which involves adapting models without access to source data, is both demanding and challenging. Existing SFDA techniques typically rely on pseudo-labels generated from confidence levels, leading to negative transfer due to significant noise. To tackle this problem, Energy-Based Pseudo-Label Refining (EBPR) is proposed for SFDA. Pseudo-labels are created for all sample clusters according to their energy scores. Global and class energy thresholds are computed to selectively filter pseudo-labels. Furthermore, a contrastive learning strategy is introduced to filter difficult samples, aligning them with their augmented versions to learn more discriminative features. Our method is validated on the Office-31, Office-Home, and VisDA-C datasets, consistently finding that our model outperformed state-of-the-art methods.
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