4 papers
DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression
Hongxu Ma, Lin Wang, Chenghou Jin +6
Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision. T…
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…
DEFT: Distilling Entangled Factors by Preventing Information Diffusion
Jiantao Wu, Lin Wang, Bo Yang +3
Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning. Many works achieve disentanglement upon information b…
Disentangling Action Sequences: Discovering Correlated Samples
Jiantao Wu, Lin Wang
Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the perform…