This paper proposes a way to break the spell of total correlation in betaTCVAE based on the motivation of the total correlation decomposition. An iterative decomposition path of total correlation is proposed, and an explanation for representation learning ability of VAE from the perspective of model capacity allocation. Newly developed objective function combines latent variable dimensions into joint distribution while relieving independent distribution constraint of the marginal distribution in combination, leading to latent variables with a more manipulable prior distribution. The novel model enables VAE to adjust the parameter capacity to divide dependent and independent data features flexibly. Experimental results on various datasets show an interesting relevance between model capacity and the latent variable grouping size, called the "V"-shaped best ELBO trajectory. Additional experiments demonstrate that the proposed method obtains better disentanglement performance with reasonable parameter capacity allocation. Finally, we design experiments to show the limitations of estimating total correlation with mutual information, identifying its source of estimation deviation.

翻译：本文根据总相关分解的动机,提出了打破乙型TCVAE中总相关性拼写的方法。建议了全相关迭接分解路径,并从模型能力分配的角度解释了VAE的代表学习能力。新开发的客观功能将潜在变量纳入联合分布,同时缓解了边际分布的单独分布限制,从而导致隐性变量,在先前的分布上更易操作。新模式使VAE能够调整参数能力,以灵活地区分依赖性和独立数据特征。各种数据集的实验结果显示模型能力与潜在变量组合大小之间的关联性,称为“V”形最佳ELBO轨迹。其他实验表明,拟议方法在合理参数能力分配方面获得了更好的分解性。最后,我们设计了实验,以显示估算与相互信息的总体相关性的局限性,并查明其估计偏离的来源。