5 papers · 1 filter
PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA
Yuan-Hao Wei, Yan-Jie Sun
Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures under statistical independence assumpt…
AR-Flow VAE: A Structured Autoregressive Flow Prior Variational Autoencoder for Unsupervised Blind Source Separation
Yuan-Hao Wei, Fu-Hao Deng, Lin-Yong Cui +1
Blind source separation (BSS) seeks to recover latent source signals from observed mixtures. Variational autoencoders (VAEs) offer a natural perspective for this problem: the laten…
Structured Kernel Regression VAE: A Computationally Efficient Surrogate for GP-VAEs in ICA
Yuan-Hao Wei, Fu-Hao Deng, Lin-Yong Cui +1
The interpretability of generative models is considered a key factor in demonstrating their effectiveness and controllability. The generated data are believed to be determined by l…
Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis
Yuan-Hao Wei, Yan-Jie Sun
This study advances the Variational Autoencoder (VAE) framework by addressing challenges in Independent Component Analysis (ICA) under both determined and underdetermined condition…
Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mapping
Yuan-Hao Wei, Yan-Jie Sun, Chen Zhang
Inference and inverse problems are closely related concepts, both fundamentally involving the deduction of unknown causes or parameters from observed data. Bayesian inference, a po…