CAD-VAE：利用相关性感知潜变量实现全面公平解耦
CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement
AI总结：
针对深度生成模型易编码敏感属性引发公平性问题、且天然相关因素下严格独立解耦不现实的问题，提出CAD-VAE通过相关潜码与条件互信息最小化实现公平解耦，实验验证其表征、反事实生成与公平编辑效果更优。
摘要：
深度生成模型虽大幅推动了表征学习发展，但可能因将敏感属性与预测特征共同编码，继承甚至放大偏见与公平性问题。当目标因素与敏感因素天然存在相关性时，在解耦过程中强制要求二者严格独立往往不切实际。为应对该挑战，我们提出CAD-VAE（相关性感知解耦变分自编码器），引入相关潜码捕捉目标属性与敏感属性间的共享信息。基于该相关潜码，我们的方法无需额外领域知识，通过直接最小化目标编码与敏感编码间的条件互信息，即可有效分离重叠因素；同时采用相关性驱动的优化策略，高效捕捉核心相关特征、消除冗余，优化相关潜码。在基准数据集上开展的大量实验表明，CAD-VAE可生成更公平的表征、更逼真的反事实样本，还能提升公平感知图像编辑效果，源代码可在https://github.com/merry7cherry/CAD-VAE获取。
英文摘要：
While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside predictive features. Enforcing strict independence in disentanglement is often unrealistic when target and sensitive factors are naturally correlated. To address this challenge, we propose \textbf{CAD-VAE} (\textbf{C}orrelation-\textbf{A}ware \textbf{D}isentangled \textbf{VAE}), which introduces a correlated latent code to capture the information shared between the target and sensitive attributes. Given this correlated latent, our method effectively separates overlapping factors without extra domain knowledge by directly minimizing the conditional mutual information between target and sensitive codes. A relevance-driven optimization strategy refines the correlated code by efficiently capturing essential correlated features and eliminating redundancy. Extensive experiments on benchmark datasets demonstrate that CAD-VAE produces fairer representations, realistic counterfactuals, and improved fairness-aware image editing. Source code is available : https://github.com/merry7cherry/CAD-VAE
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。
