想了解解耦表示学习新进展？这篇CoDID方法专门处理隐藏相关性，动态调整模式数，性能还很强。
这篇论文提出CoDID（Coordinated Disentanglement with Iterative mode Discovery），一种用于解耦表示学习的新框架。CoDID通过动态架构自适应调整模式数量，并采用元优化的协调机制缓解迭代中的误差放大。实验在多个任务上取得最先进性能，验证了处理隐藏相关性的有效性。
Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations
Disentangled representation learning is a powerful paradigm for robust attribute prediction. While recent methods address attribute correlations, hidden correlations remain underexplored, where data under the value of a certain attribute exhibit underlying modes correlated with other attributes. To preserve mode information and achieve disentanglement, we jointly discover modes and enforce mode-based conditional independence. Yet, the interdependency between these two modules may lead to error amplification under naive iterations. We propose Coordinated Disentanglement with Iterative mode Discovery (CoDID), an end-to-end framework featuring a dynamic architecture that adapts to evolving number of modes, and a coordination mechanism that mitigates error amplification via meta-optimization. Empirical results demonstrate the state-of-the-art performance on diverse tasks.