融合共享字典的因果解耦表征学习OA

Disentangled representation learning aims to discover latent factors with independent semantics from high-dimensional data,which is crucial for enhancing model interpretability and generalization ability.However,existing methods still have limitations in modeling complex causal relationships,integrating prior causal knowledge,and achieving sample-level alignment,resulting in entangled or ambiguous semantics in the learned representations.To address these issues,we propose a shared dictionary-driven causal disentanglement framework(SD-CDF)to precisely identify and structurally decouple latent causal factors.Firstly,a sparse shared dictionary convolution with skip connections is introduced.By sharing the convolutional dictionary between the encoder and decoder and combining skip connections,it effectively captures multi-scale sparse features of the data while ensuring structural interpretability,thereby improving the latent space structure.Secondly,a continuous-time causal flow is designed to model the change process of latent factors as a continuous-time dynamical system,explicitly modeling the dependencies between factors in the latent space,overcoming the limitations of static structure models.Finally,a causal effect propagation mechanism combining graph attention is employed to achieve adaptive propagation and aggregation of effects.Experimental results on synthetic datasets(Pendulum,C3dtree)and real datasets(CelebA)dem-onstrate that the proposed framework outperforms the baseline methods in standard disentanglement metrics and causal image generation intervention tasks.