通过公平干预与校正采样打破条件生成的虚假因果关系
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
AI总结：
本文提出FICS两步策略，通过公平干预强调少数样本生成，并用校正采样过滤样本以符合潜在属性分布，从而消除条件生成模型中的虚假因果关系。
摘要：
为了捕捉样本与标签之间的关系，条件生成模型常常会继承训练数据集中的虚假相关性。这可能导致标签条件分布在另一个潜在属性上不平衡。为了缓解这一问题，我们称之为条件生成的虚假因果关系，我们提出了一种通用的两步策略。(a) 公平干预（FI）：强调那些由于训练数据集中的虚假相关性而难以生成的少数样本。(b) 校正采样（CS）：显式过滤生成的样本，并确保它们遵循期望的潜在属性分布。我们设计的公平干预方法适用于对虚假属性具有不同程度监督的场景，包括无监督、弱监督和半监督场景。我们的实验结果表明，FICS能够有效解决各种数据集中条件生成的虚假因果关系。
英文摘要：
To capture the relationship between samples and labels, conditional generative models often inherit spurious correlations from the training dataset. This can result in label-conditional distributions that are imbalanced with respect to another latent attribute. To mitigate this issue, which we call spurious causality of conditional generation, we propose a general two-step strategy. (a) Fairness Intervention (FI): emphasize the minority samples that are hard to generate due to the spurious correlation in the training dataset. (b) Corrective Sampling (CS): explicitly filter the generated samples and ensure that they follow the desired latent attribute distribution. We have designed the fairness intervention to work for various degrees of supervision on the spurious attribute, including unsupervised, weakly-supervised, and semi-supervised scenarios. Our experimental results demonstrate that FICS can effectively resolve spurious causality of conditional generation across various datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
