7 papers
StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery
Yuan-Hao Wei
This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead of using an encoder to infer l…
StrADiff: A Structured Source-Wise Adaptive Diffusion Framework for Linear and Nonlinear Blind Source Separation
Yuan-Hao Wei
This paper presents StrADiff, a Structured Source-Wise Adaptive Diffusion Framework for unsupervised blind source separation under linear and nonlinear mixing. The framework treats…
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…
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…
VAEs and GANs: Implicitly Approximating Complex Distributions with Simple Base Distributions and Deep Neural Networks -- Principles, Necessity, and Limitations
Yuan-Hao Wei
This tutorial focuses on the fundamental architectures of Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), disregarding their numerous variations, to highl…