3 papers
cs.LG2025
Convolution-Based Converter : A Weak-Prior Approach For Modeling Stochastic Processes Based On Conditional Density Estimation
Chaoran Pang, Lin Wang, Shuangrong Liu +4
In this paper, a Convolution-Based Converter (CBC) is proposed to develop a methodology for removing the strong or fixed priors in estimating the probability distribution of target…
cs.LG2025
Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences
Xingshen Zhang, Lin Wang, Shuangrong Liu +3
In this study, Disentanglement in Difference(DiD) is proposed to address the inherent inconsistency between the statistical independence of latent variables and the goal of semanti…
cs.LG2023
Variantional autoencoder with decremental information bottleneck for disentanglement
Jiantao Wu, Shentong Mo, Xiang Yang +4
One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity. Previous studies, which increase…