Unwanted samples from private source categories in the learning objective of a partial domain adaptation setup can lead to negative transfer and reduce classification performance. Existing methods, such as re-weighting or aggregating target predictions, are vulnerable to this issue, especially during initial training stages, and do not adequately address overlapping categorical distributions. We propose a solution to overcome these limitations by exploring beyond the first-order moments for robust alignment of categorical distributions. We employ objectives that optimize the intra and inter-class distributions in a domain-invariant fashion and design a robust pseudo-labeling for efficient target supervision. Our approach incorporates a complement entropy objective module to reduce classification uncertainty and flatten incorrect category predictions. The experimental findings and ablation analysis of the proposed modules demonstrate the superior performance of our proposed model compared to benchmarks.

我们提出了一种解决方案，通过探索超过一阶矩来稳健地对齐分类分布，克服了现有方法在部分领域适应设置中由私人来源类别的不希望样本引起的负转移和降低分类性能的问题，同时设计了强健的伪标注以实现高效的目标监督。实验结果和消融分析表明，我们提出的模型相比基准模型表现更优。

偏域自适应的鲁棒类条件分布对齐